SES and Microbiome code
Darlene Dai
2024-10-28
Explore the association between SES factors and early-life factors (Figure 1)
Generate SES Index using CFA model (SES at enrollment) (Figure 1a)
rm(list=ls())
library(polycor)
library(psych)
library(circlize)
library(lavaan)
library(semTools)
library(colorspace)
library(ggplot2)
library(tibble)
library(limma)
library(tidyverse)
library(patchwork)
work.dir <- "/Users/darlene.dai/Desktop/SES/Results/manuscript/Code/"
setwd(work.dir)
############################################################################################
load("Results/P00 Clinical data process.RData")
set.seed(3243432)
pa1.vars <- c("income_pre" ,"meducationhighest_pre18w","deducationhighest_pre18w",
"canadaladder_pre18w", "communityladder_pre18w")
update.model <- '
meducationhighest_pre18w ~~ deducationhighest_pre18w
canadaladder_pre18w ~~ communityladder_pre18w
'
pa.model.1 <- paste0("PA",1,"_latent =~ ", paste(get(paste0("pa",1,".vars")),collapse="+"))
set.seed(3243432)
data.ana <- na.omit(data.all[,c("subjectnumber",pa1.vars)])
individual.cfa.raw <- cfa(paste(pa.model.1, update.model, sep="\n"),
data=data.ana,
ordered=c("income_pre","meducationhighest_pre18w","deducationhighest_pre18w"))
#(modindices(individual.cfa.raw, alpha=0.05, sort=T))
summary(individual.cfa.raw, standard=T,fit=T)## lavaan 0.6-19 ended normally after 30 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 20
##
## Number of observations 2752
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 2.788 8.825
## Degrees of freedom 3 3
## P-value (Chi-square) 0.426 0.032
## Scaling correction factor 0.321
## Shift parameter 0.130
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 6945.424 4811.292
## Degrees of freedom 10 10
## P-value 0.000 0.000
## Scaling correction factor 1.444
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 0.999
## Tucker-Lewis Index (TLI) 1.000 0.996
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.027
## 90 Percent confidence interval - lower 0.000 0.007
## 90 Percent confidence interval - upper 0.031 0.048
## P-value H_0: RMSEA <= 0.050 0.999 0.968
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.008 0.008
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PA1_latent =~
## income_pre 1.000 0.732 0.732
## mdctnhghst_p18 0.834 0.031 26.564 0.000 0.610 0.610
## ddctnhghst_p18 0.729 0.032 23.067 0.000 0.534 0.534
## candlddr_pr18w 1.492 0.060 24.850 0.000 1.092 0.712
## cmmntylddr_p18 0.959 0.058 16.392 0.000 0.702 0.438
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv
## .meducationhighest_pre18w ~~
## .ddctnhghst_p18 0.287 0.019 15.443 0.000 0.287
## .canadaladder_pre18w ~~
## .cmmntylddr_p18 0.582 0.045 12.840 0.000 0.582
## Std.all
##
## 0.428
##
## 0.375
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .candlddr_pr18w 6.777 0.033 208.167 0.000 6.777 4.417
## .cmmntylddr_p18 6.711 0.035 193.395 0.000 6.711 4.190
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## income_pre|t1 -1.097 0.030 -36.656 0.000 -1.097 -1.097
## income_pre|t2 -0.078 0.024 -3.278 0.001 -0.078 -0.078
## income_pre|t3 0.678 0.026 26.072 0.000 0.678 0.678
## mdctnhghs_18|1 -1.423 0.035 -40.490 0.000 -1.423 -1.423
## mdctnhghs_18|2 -0.360 0.024 -14.719 0.000 -0.360 -0.360
## mdctnhghs_18|3 0.864 0.027 31.501 0.000 0.864 0.864
## ddctnhghs_18|1 -1.066 0.030 -36.081 0.000 -1.066 -1.066
## ddctnhghs_18|2 -0.057 0.024 -2.363 0.018 -0.057 -0.057
## ddctnhghs_18|3 0.999 0.029 34.725 0.000 0.999 0.999
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .income_pre 0.464 0.464 0.464
## .mdctnhghst_p18 0.628 0.628 0.628
## .ddctnhghst_p18 0.715 0.715 0.715
## .candlddr_pr18w 1.160 0.052 22.395 0.000 1.160 0.493
## .cmmntylddr_p18 2.073 0.062 33.631 0.000 2.073 0.808
## PA1_latent 0.536 0.025 21.367 0.000 1.000 1.000PA1_latent.raw <- lavPredict(individual.cfa.raw)
data.ses.mr.raw <- data.frame(subjectnumber = data.ana$subjectnumber,
MR1_latent.raw = PA1_latent.raw[,1])
data.ses.mr <- data.ses.mr.raw
data.withmissing <- data.all[,c("subjectnumber",colnames(data.all)[colnames(data.all)%in%all.vars.use$Vars])]
data.withmissing <- merge(data.withmissing, data.ses.mr,by="subjectnumber",all=T)
data.withmissing$income_pre_num <- as.numeric(data.withmissing$income_pre)
data.withmissing$meducationhighest_pre18w_num <- as.numeric(data.withmissing$meducationhighest_pre18w)
data.withmissing$deducationhighest_pre18w_num <- as.numeric(data.withmissing$deducationhighest_pre18w)
#Different way of generating ses index - using PCA analysis
# data.try <- data.withmissing[,c("subjectnumber","income_pre_num","meducationhighest_pre18w_num","deducationhighest_pre18w_num","communityladder_pre18w","canadaladder_pre18w")]
# data.try <- na.omit(data.try)
# rownames(data.try) <- data.try$subjectnumber
# data.try <- data.try[,c("income_pre_num","meducationhighest_pre18w_num","deducationhighest_pre18w_num","communityladder_pre18w","canadaladder_pre18w")]
#
# pca.m <- prcomp(data.try,scale. = T)
# pca.m$rotation
# summ <- summary(pca.m)
# weights <- summ$importance[2,]
# data.try1 <- data.frame(pc.avg1=apply(pca.m$x[,1,drop=F],1,mean))
# data.try2 <- data.frame(pc.avg2=apply(pca.m$x[,1:2,drop=F], 1, function(x) sum(x * weights[1:2])))
# data.try3 <- data.frame(pc.avg3=apply(pca.m$x[,1:3,drop=F], 1, function(x) sum(x * weights[1:3])))
# data.try5 <-data.frame(pc.avg5=apply(pca.m$x[,1:5,drop=F], 1, function(x) sum(x * weights[1:5])))
#
# data.try <- cbind(data.try1,data.try2,data.try3,data.try5)
# dd.kk <- merge(data.ses.mr.raw, data.try5,by.x="subjectnumber", by.y="row.names")
# library(reshape2)
# library(ggpubr)
# dd.kk.long <- melt(dd.kk, id.vars=c("subjectnumber","MR1_latent.raw"))
# ggplot(dd.kk.long, aes(MR1_latent.raw,value))+
# geom_point()+
# facet_wrap(~variable,scale="free")+stat_cor(method="pearson")
# data.withmissing <- merge(data.withmissing, data.try5, by.x="subjectnumber", by.y="row.names",all.x=T)Generate SES Index at 1 year using CFA model (assocaited with SES index at enrollment correlation=0.86)
#1y SES cfa Model
pa1.vars <- c("income_1y" ,"meducationhighest_1y","deducationhighest_1y",
"canadaladder_1y", "communityladder_1y")
update.model <- '
meducationhighest_1y ~~ deducationhighest_1y
canadaladder_1y ~~ communityladder_1y
'
pa.model.1 <- paste0("PA",1,"_latent =~ ", paste(get(paste0("pa",1,".vars")),collapse="+"))
set.seed(3243432)
data.ana <- na.omit(data.all[,c("subjectnumber",pa1.vars)])
individual.cfa.raw <- cfa(paste(pa.model.1, update.model, sep="\n"),
data=data.ana,
ordered=c("income_1y","meducationhighest_1y","deducationhighest_1y"))
summary(individual.cfa.raw, standard=T,fit=T)## lavaan 0.6-19 ended normally after 31 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 20
##
## Number of observations 2191
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 4.537 14.120
## Degrees of freedom 3 3
## P-value (Chi-square) 0.209 0.003
## Scaling correction factor 0.324
## Shift parameter 0.105
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 4745.897 3313.744
## Degrees of freedom 10 10
## P-value 0.000 0.000
## Scaling correction factor 1.433
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 0.997
## Tucker-Lewis Index (TLI) 0.999 0.989
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.015 0.041
## 90 Percent confidence interval - lower 0.000 0.021
## 90 Percent confidence interval - upper 0.042 0.064
## P-value H_0: RMSEA <= 0.050 0.988 0.710
## P-value H_0: RMSEA >= 0.080 0.000 0.002
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.011 0.011
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## PA1_latent =~
## income_1y 1.000 0.706 0.706
## medctnhghst_1y 0.790 0.040 19.699 0.000 0.557 0.557
## dedctnhghst_1y 0.699 0.041 17.249 0.000 0.493 0.493
## canadaladdr_1y 1.521 0.082 18.469 0.000 1.074 0.706
## commntylddr_1y 1.040 0.073 14.178 0.000 0.734 0.471
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .meducationhighest_1y ~~
## .dedctnhghst_1y 0.321 0.021 15.250 0.000 0.321 0.445
## .canadaladder_1y ~~
## .commntylddr_1y 0.661 0.054 12.287 0.000 0.661 0.446
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .canadaladdr_1y 6.825 0.036 190.280 0.000 6.825 4.486
## .commntylddr_1y 6.776 0.038 177.793 0.000 6.776 4.350
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## income_1y|t1 -1.054 0.033 -31.974 0.000 -1.054 -1.054
## income_1y|t2 0.099 0.027 3.695 0.000 0.099 0.099
## income_1y|t3 0.878 0.031 28.430 0.000 0.878 0.878
## mdctnhghst_1|1 -1.494 0.041 -36.398 0.000 -1.494 -1.494
## mdctnhghst_1|2 -0.410 0.028 -14.843 0.000 -0.410 -0.410
## mdctnhghst_1|3 0.839 0.031 27.496 0.000 0.839 0.839
## ddctnhghst_1|1 -1.105 0.034 -32.824 0.000 -1.105 -1.105
## ddctnhghst_1|2 -0.076 0.027 -2.840 0.005 -0.076 -0.076
## ddctnhghst_1|3 0.960 0.032 30.210 0.000 0.960 0.960
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .income_1y 0.502 0.502 0.502
## .medctnhghst_1y 0.689 0.689 0.689
## .dedctnhghst_1y 0.757 0.757 0.757
## .canadaladdr_1y 1.162 0.067 17.407 0.000 1.162 0.502
## .commntylddr_1y 1.888 0.069 27.504 0.000 1.888 0.778
## PA1_latent 0.498 0.032 15.812 0.000 1.000 1.000PA1_latent.raw <- lavPredict(individual.cfa.raw)
data.ses.mr.raw <- data.frame(subjectnumber =data.ana$subjectnumber,
MR1_latent.raw.1y = PA1_latent.raw[,1])
data.ses.mr <- data.ses.mr.raw
data.withmissing <- merge(data.withmissing, data.ses.mr,by="subjectnumber",all=T)
#SES Pregnancy and SES at 1 year index are high associated
with(data.withmissing, cor(MR1_latent.raw,MR1_latent.raw.1y,use="pairwise"))## [1] 0.8625438#Clean the data
cleanData <- function(data.ana){
data.ana$income_pre_num <- as.numeric(unclass(as.factor(data.ana$income_pre)))
data.ana$cwol <- ifelse(data.ana$birth_mode=="3.cwol",1,0)
data.ana$cwl <- ifelse(data.ana$birth_mode=="2.cwl",1,0)
data.ana$vaginal <- ifelse(data.ana$birth_mode=="1.vaginal",1,0)
data.ana$mom_education_highest_num <- as.numeric(unclass(as.factor(data.ana$meducationhighest_pre18w)))
data.ana$dad_education_highest_num <- as.numeric(unclass(as.factor(data.ana$deducationhighest_pre18w)))
data.ana$race_dad <- relevel(as.factor(data.ana$race_dad), ref="Caucasian White")
data.ana$race_mom <- relevel(as.factor(data.ana$race_mom), ref="Caucasian White")
data.ana$causacian_mom <- ifelse(data.ana$race_mom=="Caucasian White",1,0)
data.ana$causacian_dad <- ifelse(data.ana$race_dad=="Caucasian White",1,0)
data.ana$summer <- ifelse(data.ana$Season=="2.Summer",1,0)
data.ana$fall <- ifelse(data.ana$Season=="3.Fall",1,0)
data.ana$winter <- ifelse(data.ana$Season=="4.Winter",1,0)
data.ana$vancouver <- ifelse(data.ana$site=="vancouver",1,0)
data.ana$toronto <- ifelse(data.ana$site=="toronto",1,0)
data.ana$winnipeg <- ifelse(data.ana$site=="winnipeg",1,0)
data.ana$edmonton <- ifelse(data.ana$site=="edmonton",1,0)
data.ana$atbx.1y.time[data.ana$atbx.1y==0& !is.na(data.ana$atbx.1y)] <- 999
return(data.ana)
}
data.withmissing <- cleanData(data.withmissing)
cutpoints <- quantile(data.withmissing$MR1_latent.raw,na.rm=T)
cutpoints[1] <- min(data.withmissing$MR1_latent.raw,na.rm=T)-0.1
cutpoints[5] <- max(data.withmissing$MR1_latent.raw,na.rm=T)+0.1
data.withmissing$cat_SES <- cut(data.withmissing$MR1_latent.raw,
breaks=cutpoints,
labels=c('1.0-25%', '2.25-50%', '3.50-75%', '4.75-100%'),na.rm=T)
data.withmissing$MR1_latent.raw.interquartile <- data.withmissing$MR1_latent.raw/(cutpoints[4]-cutpoints[2])
data.withmissing$EBF_6m <- ifelse(data.withmissing$ebf_duration>=6,1,0)
data.withmissing$SES_EBFcompound <- ifelse(data.withmissing$MR1_latent.raw>cutpoints[3] & data.withmissing$EBF_6m==1,"4.High SES w/ EBF to 6m",
ifelse(data.withmissing$MR1_latent.raw>cutpoints[3] & data.withmissing$EBF_6m==0,"3.High SES w/o EBF to 6m",
ifelse(data.withmissing$MR1_latent.raw<cutpoints[3] & data.withmissing$EBF_6m==1, "2.Low SES w/ EBF to 6m",
ifelse(data.withmissing$MR1_latent.raw<cutpoints[3] & data.withmissing$EBF_6m==0, "1.Low SES w/o EBF to 6m",NA))))
cutpoints.1y <- quantile(data.withmissing$MR1_latent.raw.1y,na.rm=T)
cutpoints.1y[1] <- min(data.withmissing$MR1_latent.raw.1y,na.rm=T)-0.1
cutpoints.1y[5] <- max(data.withmissing$MR1_latent.raw.1y,na.rm=T)+0.1
data.withmissing$cat_SES.1y <- cut(data.withmissing$MR1_latent.raw.1y,
breaks=cutpoints.1y,
labels=c('1.0-25%', '2.25-50%', '3.50-75%', '4.75-100%'),na.rm=T)
data.withmissing$MR1_latent.raw.interquartile.1y <- data.withmissing$MR1_latent.raw.1y/(cutpoints.1y[4]-cutpoints.1y[2])
data.map <- rbind(data.map,
data.frame(Vars=c("MR1_latent.raw", "MR1_latent.raw.1y",
"MR1_latent.raw.interquartile","MR1_latent.raw.interquartile.1y",
"dad_education_highest_num" ,
"mom_education_highest_num",
"income_pre_num",
"SES_EBFcompound"),
Category=c(rep("SES",8)),
Label=c("SES Index","SES Index (1y)",
"SES Index","SES Index (1y)",
"Education of father",
"Education of mother",
"Income",
"SES and Breastfeeding Group"),
Type=c(rep("Continuous",7),"Categorical"),
Values=NA))
data.map <- unique(data.map)
save(data.withmissing,
output.vars, clic.vars, data.map,
file="Results/P0 Clinical data with SES Index.RData")Generate Demographic table (Table S1)
###########################################################################################
#demographic table
used.vars <- c("asthma5y_ep","atopy5y","Tscore_abnormal","overweight",
"atbx.1y", "atbx.m.birth", "atbx.m.preg", "awayhome1h_1y" ,
"awayhome1h_3m", "bf_duration_imp","EBF_6m","bw_sd", "canue_Zdweldensity_pre18w" ,
"cat_1y", "condition_GestationalDiabetes_mother_prelabour",
"dog_1y", "fasthma","father_atopy_1y",
"freq_score_basic", "gest_days", "hometype_3m","masthma","maternaldistress_perceived_1y",
"maternaldistress_perceived_pre18w", "mom_bmi_best", "mom_UPFContr", "mother_atopy_1y",
"no2.preg", "no2.y1", "num_siblings", "postnatal_smoke",
"Pregnancy.Tdens250", "prenatal_smoke", "rural", "furniture_densityclutter_3m",
"home_plant_3m", "floor_carpet_3m", "mould_3m_mh",
"birth_mode", "Season",
"MR1_latent.raw","meducationhighest_pre18w","deducationhighest_pre18w","income_pre", "canadaladder_pre18w", "communityladder_pre18w",
"MR1_latent.raw.1y","income_1y" ,"meducationhighest_1y","deducationhighest_1y", "canadaladder_1y", "communityladder_1y",
"race_dad","race_mom","site","male")
# ,
# "bfhistory_m","bfhistory_m_time",
# "bfhistory_f","bfhistory_f_time",
# "PBscore",
# "WSscore",
# "BAscore","eaPBscore",
# "eaWSscore" ,
# "eaBAscore",
# "pb_sumscore",
# "ws_sumscore",
# "ba_sumscore" ,
# "eapb_sumscore",
# "eaws_sumscore",
# "eaba_sumscore")
used.vars.demo <- used.vars
#Dictionary for demographic table and forest plot
level.dictionary <- c("2.cwl" = "C-section with labor",
"3.cwol" = "C-section without labor",
"1.vaginal" = "Vaginal",
"1.Spring" = "Spring",
"2.Summer" = "Summer",
"3.Fall" = "Fall",
"4.Winter" = "Winter",
"4.High SES w/ EBF to 6m" = "High SES w/ EBF to 6m",
"3.High SES w/o EBF to 6m" = "High SES w/o EBF to 6m" ,
"2.Low SES w/ EBF to 6m" = "Low SES w/ EBF to 6m",
"1.Low SES w/o EBF to 6m" = "Low SES w/o EBF to 6m")
map.labels <- c(as.character(data.map$Label))
names(map.labels) <- c(as.character(data.map$Vars))
cate.vars.all <- data.map$Vars[data.map$Type=="Categorical"]
cont.vars.all <- data.map$Vars[data.map$Type=="Continuous"]
demo.tab1 <- demo.fuc(data.demo = data.withmissing,
cate.vars = used.vars.demo[used.vars.demo%in%cate.vars.all],
cont.vars = c("MR1_latent.raw",used.vars.demo[used.vars.demo%in%cont.vars.all]),
subg.var = NA,
comp.p=F,
dic.demo = map.labels,
na.option = "Exclude",
dic.level = level.dictionary)
demo.tab1.na <- demo.fuc(data.demo = data.withmissing,
cate.vars = used.vars.demo[used.vars.demo%in%cate.vars.all],
cont.vars = c("MR1_latent.raw",used.vars.demo[used.vars.demo%in%cont.vars.all]),
subg.var = NA,
comp.p=F,
dic.demo = map.labels,
na.option = "Include",
dic.level = level.dictionary)
demo.tab1.na <- subset(demo.tab1.na, Variable=="Unknown")
demo.tab1 <- rbind(demo.tab1, demo.tab1.na)
demo.tab1 <- demo.tab1[order(demo.tab1$Map),]
demo.tab1 <- merge(demo.tab1, data.map, by.x="Map", by.y="Vars",all.x=T,sort = F)
demo.tab1 <- demo.tab1[,c("Category","Variable","Cohort","Map")]
demo.tab1.use <- demo.tab1
demo.tab1.use$Category[is.na(demo.tab1.use$Category)] <- "no.patient"
demo.tab1.use$Category <- factor(demo.tab1.use$Category, levels=c("no.patient","SES","Ethnicity","Health",
"Birth", "Pregnancy", "Postnatal" ,
"Home Environment" , "Neighbourhood Environment","Parental Diet/Health" ),
ordered=T)
demo.tab1.use <- subset(demo.tab1.use, !Category%in%c("Ethnicity"))
write.csv(demo.tab1.use, "Results/Demographic table.csv")
demo.tab1.use <- demo.tab1.use[order(demo.tab1.use$Category),c("Variable","Cohort")]
demo.tab1.use## Variable Cohort
## 231 No. patients 3263
## 31 Social status (Canada) (1y)
## 32 Median (Range) 7 (1, 10)
## 33 IQR (Q1,Q3) 6, 8
## 34 Unknown 738 (22.6%)
## 35 Social status (Canada)
## 36 Median (Range) 7 (1, 10)
## 37 IQR (Q1,Q3) 6, 8
## 38 Unknown 190 (5.8%)
## 46 Social status (community) (1y)
## 47 Median (Range) 7 (1, 10)
## 48 IQR (Q1,Q3) 6, 8
## 49 Unknown 737 (22.6%)
## 50 Social status (community)
## 51 Median (Range) 7 (1, 10)
## 52 IQR (Q1,Q3) 6, 8
## 53 Unknown 193 (5.9%)
## 56 Education of father (1y), n(%)
## 57 1.highschool 466 (15.2%)
## 58 2.college 1022 (33.3%)
## 59 3.university 1073 (35%)
## 60 4.masterorphd 508 (16.6%)
## 61 Unknown 194 (5.9%)
## 62 Education of father, n(%)
## 63 1.highschool 475 (15.3%)
## 64 2.college 1038 (33.5%)
## 65 3.university 1096 (35.3%)
## 66 4.masterorphd 494 (15.9%)
## 67 Unknown 160 (4.9%)
## 88 Income (1y), n(%)
## 89 1.0-49999 368 (15.6%)
## 90 2.5000-9999 917 (39%)
## 91 3.100000-149999 631 (26.8%)
## 92 4.150000over 436 (18.5%)
## 93 Unknown 911 (27.9%)
## 94 Income, n(%)
## 95 1.0-49999 399 (14.1%)
## 96 2.5000-9999 943 (33.2%)
## 97 3.100000-149999 795 (28%)
## 98 4.150000over 700 (24.7%)
## 99 Unknown 426 (13.1%)
## 113 Education of mother (1y), n(%)
## 114 1.highschool 271 (8.7%)
## 115 2.college 894 (28.8%)
## 116 3.university 1340 (43.1%)
## 117 4.masterorphd 603 (19.4%)
## 118 Unknown 155 (4.8%)
## 119 Education of mother, n(%)
## 120 1.highschool 276 (8.8%)
## 121 2.college 905 (28.9%)
## 122 3.university 1353 (43.2%)
## 123 4.masterorphd 598 (19.1%)
## 124 Unknown 131 (4%)
## 219 SES Index
## 220 Median (Range) 0.1 (-2.4, 1.6)
## 221 IQR (Q1,Q3) -0.4, 0.4
## 222 SES Index
## 223 Median (Range) 0.1 (-2.4, 1.6)
## 224 IQR (Q1,Q3) -0.4, 0.4
## 225 Unknown 511 (15.7%)
## 226 Unknown 511 (15.7%)
## 227 SES Index (1y)
## 228 Median (Range) 0.1 (-2.4, 1.6)
## 229 IQR (Q1,Q3) -0.4, 0.4
## 230 Unknown 1072 (32.9%)
## 9 Atopy at 5y, n(%)
## 10 494 (19.3%)
## 11 Unknown 708 (21.7%)
## 210 Aasthma at 5y, n(%)
## 211 165 (6.9%)
## 212 Unknown 864 (26.5%)
## 213 Overweight at 5y, n(%)
## 214 542 (20.6%)
## 215 Unknown 638 (19.6%)
## 216 Abnormal child behavior at 5y, n(%)
## 217 172 (7.3%)
## 218 Unknown 903 (27.7%)
## 4 Intrapartum antibiotics, n(%)
## 5 651 (27%)
## 6 Unknown 856 (26.2%)
## 22 Delivery mode, n(%)
## 23 Vaginal 2412 (74.8%)
## 24 C-section with labor 425 (13.2%)
## 25 C-section without labor 387 (12%)
## 26 Unknown 39 (1.2%)
## 27 Birth weight Z score
## 28 Median (Range) -0.1 (-3.1, 4.3)
## 29 IQR (Q1,Q3) -0.7, 0.5
## 30 Unknown 75 (2.3%)
## 81 Gestational age
## 82 Median (Range) 278 (238, 300)
## 83 IQR (Q1,Q3) 272, 283
## 84 Unknown 54 (1.7%)
## 185 Season of birth, n(%)
## 186 Spring 889 (27.2%)
## 187 Summer 830 (25.4%)
## 188 Fall 754 (23.1%)
## 189 Winter 790 (24.2%)
## 7 Maternal antibiotics (preg.), n(%)
## 8 314 (9.6%)
## 54 Gestational diabetes, n(%)
## 55 154 (4.7%)
## 109 Maternal distress (preg.)
## 110 Median (Range) 13 (0, 35)
## 111 IQR (Q1,Q3) 9, 17
## 112 Unknown 325 (10%)
## 155 Prenatal smoking, n(%)
## 156 606 (19.1%)
## 157 Unknown 97 (3%)
## 1 Antibiotics use (1y), n(%)
## 2 619 (26.1%)
## 3 Unknown 890 (27.3%)
## 12 Reg. childcare attend. (1y), n(%)
## 13 735 (28.5%)
## 14 Unknown 688 (21.1%)
## 15 Reg. childcare attend. (3m), n(%)
## 16 338 (11.3%)
## 17 Unknown 274 (8.4%)
## 18 Any breastfeeding dur.
## 19 Median (Range) 10 (0, 30)
## 20 IQR (Q1,Q3) 5, 14
## 21 Unknown 106 (3.2%)
## 43 Pet (cat), n(%)
## 44 550 (21.4%)
## 45 Unknown 690 (21.1%)
## 68 Pet (dog), n(%)
## 69 701 (27.1%)
## 70 Unknown 681 (20.9%)
## 144 Older siblings (#)
## 145 Median (Range) 0 (0, 4)
## 146 IQR (Q1,Q3) 0, 1
## 147 Unknown 101 (3.1%)
## 148 Postnatal smoking, n(%)
## 149 620 (23.1%)
## 150 Unknown 578 (17.7%)
## 195 Exclusive breastfeeding (6m), n(%)
## 196 522 (17.1%)
## 197 Unknown 208 (6.4%)
## 77 Cleaning product use
## 78 Median (Range) 31 (4, 76)
## 79 IQR (Q1,Q3) 25, 38
## 80 Unknown 341 (10.5%)
## 85 Multifamily home, n(%)
## 86 878 (28.9%)
## 87 Unknown 223 (6.8%)
## 198 Clutter home, n(%)
## 199 581 (19.1%)
## 200 Unknown 229 (7%)
## 201 Potted plant, n(%)
## 202 1822 (59.9%)
## 203 Unknown 223 (6.8%)
## 204 Carpet floor, n(%)
## 205 1722 (56.6%)
## 206 Unknown 223 (6.8%)
## 207 Medium/high mould, n(%)
## 208 359 (11.8%)
## 209 Unknown 223 (6.8%)
## 39 Dwelling density
## 40 Median (Range) 0.2 (-0.8, 10.7)
## 41 IQR (Q1,Q3) -0.2, 1.4
## 42 Unknown 197 (6%)
## 136 Air pollution (NO2) (preg.)
## 137 Median (Range) 10.6 (1.3, 35.8)
## 138 IQR (Q1,Q3) 5.3, 15
## 139 Unknown 191 (5.9%)
## 140 Air pollution (NO2) (1y)
## 141 Median (Range) 9.1 (0.5, 30.5)
## 142 IQR (Q1,Q3) 4.6, 13.3
## 143 Unknown 194 (5.9%)
## 151 Tree density (preg.)
## 152 Median (Range) 20 (0, 99)
## 153 IQR (Q1,Q3) 13, 32
## 154 Unknown 663 (20.3%)
## 182 Rural, n(%)
## 183 186 (6%)
## 184 Unknown 146 (4.5%)
## 71 Paternal asthma, n(%)
## 72 510 (19.2%)
## 73 Unknown 600 (18.4%)
## 74 Paternal atopy, n(%)
## 75 1663 (67.7%)
## 76 Unknown 806 (24.7%)
## 102 Maternal asthma, n(%)
## 103 749 (23.4%)
## 104 Unknown 56 (1.7%)
## 105 Maternal distress (1y)
## 106 Median (Range) 12 (0, 40)
## 107 IQR (Q1,Q3) 7, 17
## 108 Unknown 685 (21%)
## 125 Maternal BMI
## 126 Median (Range) 23.3 (13.9, 56.9)
## 127 IQR (Q1,Q3) 21, 27
## 128 Unknown 327 (10%)
## 129 Maternal UPF diet
## 130 Median (Range) 46.8 (1.9, 82.9)
## 131 IQR (Q1,Q3) 39.7, 53.8
## 132 Unknown 291 (8.9%)
## 133 Maternal atopy, n(%)
## 134 1727 (57.7%)
## 135 Unknown 268 (8.2%)
## 100 Male, n(%)
## 101 1717 (52.6%)
## 190 Study center, n(%)
## 191 edmonton 768 (23.5%)
## 192 toronto 768 (23.5%)
## 193 vancouver 735 (22.5%)
## 194 winnipeg 992 (30.4%)#Load in the package and function and data
data.use <- data.withmissing
#################################################################################################################
#Used early-life factors and their labels
used.vars <- c("atbx.1y", "atbx.m.birth", "atbx.m.preg", "awayhome1h_1y" ,
"awayhome1h_3m", "bf_duration_imp","bw_sd", "canue_Zdweldensity_pre18w" ,
"cat_1y", "condition_GestationalDiabetes_mother_prelabour",
"dog_1y", "EBF_6m", "fasthma","father_atopy_1y",
"freq_score_basic", "gest_days", "hometype_3m", "masthma","maternaldistress_perceived_1y",
"maternaldistress_perceived_pre18w", "mom_bmi_best", "mom_UPFContr", "mother_atopy_1y",
"no2.preg", "no2.y1", "num_siblings", "postnatal_smoke",
"Pregnancy.Tdens250", "prenatal_smoke", "rural", "furniture_densityclutter_3m",
"home_plant_3m", "floor_carpet_3m", "mould_3m_mh", "vaginal", "fall","summer", "winter")
all.vars.use <- c(used.vars,"dad_education_highest_num","mom_education_highest_num",
"income_pre_num","communityladder_pre18w",
"canadaladder_pre18w")
data.map.use <- data.map
rownames(data.map.use) <- data.map.use$Vars
#Scale variables
data.fa.scale <- data.use[,c(all.vars.use)]
for(i in 1:ncol(data.fa.scale)){
data.fa.scale[,i] <- as.numeric(as.factor(data.fa.scale[,i]))
}
data.fa.scale <- scale(data.fa.scale)
X <- data.fa.scale[,c(all.vars.use)]
colnames(X) <- data.map.use[all.vars.use,"Label"]
#Calculate correlation
kk <- cor(X, use="pairwise", method="pearson")
result_new <- kk
pdf("Figures/P2 SES Icluster of factors.pdf", height=12, width=12)
iclust(X)
dev.off()Generate Tree diagram of Item Cluster analysis (ICLUST) (Figure S1b)
iclust(X)
## ICLUST (Item Cluster Analysis)
## Call: iclust(r.mat = X)
##
## Purified Alpha:
## C27 C32 C29 C6 C24 C15 C11 C30 C8 C10 C1
## 0.72 0.75 0.47 0.67 0.25 0.28 0.50 0.27 0.31 0.30 0.30
##
## G6* reliability:
## C27 C32 C29 C6 C24 C15 C11 C30 C8 C10 C1
## 0.54 0.83 0.50 1.00 0.59 1.00 0.25 0.48 1.00 1.00 1.00
##
## Original Beta:
## C27 C32 C29 C6 C24 C15 C11 C30 C8 C10 C1
## 0.46 0.32 0.35 0.67 0.19 0.28 0.50 0.19 0.31 0.30 0.30
##
## Cluster size:
## C27 C32 C29 C6 C24 C15 C11 C30 C8 C10 C1
## 8 8 5 2 4 2 2 6 2 2 2
##
## Item by Cluster Structure matrix:
## O P C27 C32 C29 C6 C24 C15 C11
## Antibiotics use (1y) C30 C27 0.10 -0.06 0.13 0.08 0.13 -0.01 -0.03
## Intrapartum antibiotics C30 C27 -0.06 0.02 -0.03 0.00 0.04 -0.01 0.02
## Maternal antibiotics (preg.) C30 C27 0.03 -0.03 0.04 0.05 0.02 -0.01 -0.02
## Reg. childcare attend. (1y) C8 C27 -0.05 -0.01 0.12 0.07 -0.02 -0.01 -0.01
## Reg. childcare attend. (3m) C8 C27 -0.01 -0.09 0.13 -0.01 0.00 0.04 -0.01
## Any breastfeeding dur. C29 C27 -0.07 0.32 -0.55 -0.11 -0.20 0.00 -0.02
## Birth weight Z score C24 C27 0.12 -0.07 0.08 0.01 0.30 0.03 -0.05
## Dwelling density C27 C27 -0.74 0.30 -0.22 -0.10 -0.24 0.15 0.04
## Pet (cat) C24 C32 0.01 -0.10 0.03 0.04 0.21 0.20 -0.01
## Gestational diabetes C30 C32 -0.01 -0.07 0.06 0.03 0.12 0.02 0.03
## Pet (dog) C29 C32 0.14 -0.07 0.23 -0.04 0.11 -0.03 0.01
## Exclusive breastfeeding (6m) C29 C32 -0.01 0.14 -0.38 -0.06 -0.06 0.01 -0.07
## Paternal asthma C1 C32 0.01 -0.01 0.01 0.00 -0.03 0.02 0.05
## Paternal atopy C1 C32 -0.12 0.08 -0.10 0.03 -0.12 -0.01 -0.02
## Cleaning product use C29 C32 0.03 -0.12 0.45 0.07 0.15 -0.14 0.00
## Gestational age C30 C32 0.00 0.05 -0.08 -0.03 0.09 -0.07 -0.05
## Multifamily home C27 C29 -0.38 -0.03 -0.25 0.05 -0.15 0.31 0.04
## Maternal asthma C10 C29 0.03 -0.06 0.09 0.11 0.13 0.08 0.03
## Maternal distress (1y) C6 C29 0.04 -0.23 0.08 0.62 0.05 0.20 0.00
## Maternal distress (preg.) C6 C29 0.08 -0.37 0.16 0.66 0.09 0.16 0.04
## Maternal BMI C24 C29 0.17 -0.28 0.32 0.13 0.38 0.17 0.06
## Maternal UPF diet C29 C6 0.12 -0.27 0.33 0.17 0.16 -0.04 0.01
## Maternal atopy C10 C6 -0.15 0.09 -0.04 0.01 -0.09 0.05 -0.03
## Air pollution (NO2) (preg.) C27 C24 -0.88 0.30 -0.13 -0.08 -0.16 0.07 -0.14
## Air pollution (NO2) (1y) C27 C24 -0.84 0.29 -0.09 -0.06 -0.19 0.10 -0.01
## Older siblings (#) C27 C24 0.25 -0.11 -0.03 0.05 0.16 0.05 -0.04
## Postnatal smoking C32 C24 0.07 -0.54 0.37 0.23 0.23 0.20 0.01
## Tree density (preg.) C27 C15 -0.49 0.26 0.03 -0.12 -0.15 0.06 0.05
## Prenatal smoking C32 C15 0.09 -0.52 0.33 0.22 0.19 0.18 -0.01
## Rural C27 C11 0.29 -0.18 0.10 0.00 0.04 -0.08 0.00
## Clutter home C15 C11 0.02 -0.22 0.00 0.19 0.20 0.40 0.03
## Potted plant C32 C30 -0.09 0.21 -0.17 -0.15 -0.09 -0.09 0.01
## Carpet floor C27 C30 0.27 -0.11 0.07 0.03 0.00 -0.14 -0.02
## Medium/high mould C15 C30 -0.17 -0.04 -0.08 0.02 0.02 0.33 -0.03
## Vaginal delivery C30 C30 0.10 -0.01 -0.10 -0.03 -0.11 0.01 0.02
## Born in fall C24 C30 -0.02 0.01 -0.01 0.03 -0.36 0.02 0.01
## Born in summer C11 C30 0.01 -0.02 -0.03 0.01 0.30 0.04 -0.56
## Born in winter C11 C8 0.00 -0.02 0.03 0.04 0.29 0.03 0.60
## Education of father C32 C8 -0.34 0.61 -0.37 -0.17 -0.28 -0.14 0.01
## Education of mother C32 C10 -0.32 0.67 -0.40 -0.20 -0.29 -0.16 -0.01
## Income C32 C10 -0.28 0.59 -0.03 -0.35 -0.15 -0.39 -0.01
## Social status (community) C32 C1 -0.17 0.48 -0.18 -0.30 -0.16 -0.10 0.01
## Social status (Canada) C32 C1 -0.23 0.67 -0.20 -0.38 -0.23 -0.25 0.02
## C30 C8 C10 C1
## Antibiotics use (1y) 0.24 0.01 0.07 0.00
## Intrapartum antibiotics 0.17 0.06 0.04 -0.03
## Maternal antibiotics (preg.) 0.17 0.00 -0.01 -0.05
## Reg. childcare attend. (1y) 0.06 0.36 0.11 0.06
## Reg. childcare attend. (3m) -0.03 0.34 0.09 -0.03
## Any breastfeeding dur. -0.19 -0.20 0.04 0.10
## Birth weight Z score 0.11 -0.09 -0.01 -0.11
## Dwelling density -0.03 -0.02 0.08 0.10
## Pet (cat) -0.08 -0.01 0.00 -0.05
## Gestational diabetes 0.26 0.01 0.00 -0.02
## Pet (dog) 0.02 0.12 0.00 -0.07
## Exclusive breastfeeding (6m) -0.08 -0.14 0.00 0.07
## Paternal asthma -0.01 0.02 0.03 0.33
## Paternal atopy -0.05 0.01 -0.01 0.36
## Cleaning product use 0.19 0.09 0.08 0.01
## Gestational age -0.39 0.05 -0.06 0.04
## Multifamily home 0.01 -0.01 -0.02 -0.04
## Maternal asthma 0.12 0.07 0.35 0.00
## Maternal distress (1y) 0.09 0.09 0.12 0.01
## Maternal distress (preg.) 0.12 0.02 0.09 0.03
## Maternal BMI 0.29 0.05 0.09 -0.03
## Maternal UPF diet 0.04 0.12 0.08 -0.01
## Maternal atopy 0.00 0.13 0.36 0.02
## Air pollution (NO2) (preg.) 0.07 0.07 0.20 0.13
## Air pollution (NO2) (1y) 0.10 0.05 0.21 0.12
## Older siblings (#) 0.06 -0.20 -0.05 -0.09
## Postnatal smoking 0.13 0.07 -0.03 -0.08
## Tree density (preg.) -0.05 -0.02 0.03 0.06
## Prenatal smoking 0.10 0.11 0.04 -0.01
## Rural -0.05 -0.02 -0.06 -0.11
## Clutter home 0.03 0.06 0.09 0.00
## Potted plant -0.02 -0.07 0.01 0.01
## Carpet floor 0.03 -0.06 -0.10 -0.10
## Medium/high mould 0.00 -0.03 0.03 0.01
## Vaginal delivery -0.19 -0.04 -0.08 -0.03
## Born in fall 0.04 -0.02 0.01 0.07
## Born in summer -0.05 -0.02 -0.02 0.00
## Born in winter -0.02 -0.06 -0.02 0.05
## Education of father -0.07 -0.17 0.00 0.05
## Education of mother -0.09 -0.07 0.04 0.05
## Income -0.03 -0.04 0.11 0.22
## Social status (community) -0.04 -0.10 -0.01 -0.01
## Social status (Canada) -0.10 -0.01 0.03 0.05
##
## With Sums of squares of:
## C27 C32 C29 C6 C24 C15 C11 C30 C8 C10 C1
## 2.74 2.31 1.26 1.06 0.98 0.75 0.72 0.58 0.44 0.40 0.38
##
## Purified scale intercorrelations
## reliabilities on diagonal
## correlations corrected for attenuation above diagonal:
## C27 C32 C29 C6 C24 C15 C11 C30 C8 C10 C1
## C27 0.72 -0.39 0.20 0.09 0.31 -0.19 -0.01 -0.02 -0.07 -0.15 -0.14
## C32 -0.29 0.75 -0.49 -0.43 -0.43 -0.32 0.01 -0.13 -0.13 0.03 0.09
## C29 0.12 -0.29 0.47 0.17 0.40 -0.10 0.05 0.27 0.29 0.05 -0.11
## C6 0.06 -0.30 0.10 0.67 0.11 0.23 0.02 0.14 0.06 0.12 0.03
## C24 0.13 -0.19 0.14 0.05 0.25 0.31 -0.01 0.25 -0.02 0.06 -0.19
## C15 -0.09 -0.15 -0.04 0.10 0.08 0.28 -0.01 0.03 0.03 0.13 0.01
## C11 -0.01 0.00 0.03 0.01 0.00 0.00 0.50 0.02 -0.03 0.00 0.04
## C30 -0.01 -0.06 0.09 0.06 0.07 0.01 0.01 0.27 0.04 0.14 -0.07
## C8 -0.04 -0.06 0.11 0.03 -0.01 0.01 -0.01 0.01 0.31 0.20 0.03
## C10 -0.07 0.02 0.02 0.05 0.02 0.04 0.00 0.04 0.06 0.30 0.02
## C1 -0.07 0.04 -0.04 0.01 -0.05 0.00 0.02 -0.02 0.01 0.01 0.30
##
## Cluster fit = 0.48 Pattern fit = 0.95 RMSR = 0.04Explore how SES index is associated with early life factors with adjustment of site and sensitivity analyses
#Extract response variables and covariates
outcome.vars <- data.map$Vars[data.map$Category=="Health"]
outcome.vars <- outcome.vars
cov.vars <- c("atbx.1y", "atbx.m.birth", "atbx.m.preg", "awayhome1h_1y" ,
"awayhome1h_3m", "bf_duration_imp","EBF_6m","bw_sd", "canue_Zdweldensity_pre18w" ,
"cat_1y", "condition_GestationalDiabetes_mother_prelabour",
"dog_1y", "fasthma","father_atopy_1y",
"freq_score_basic", "gest_days", "hometype_3m","masthma","maternaldistress_perceived_1y",
"maternaldistress_perceived_pre18w", "mom_bmi_best", "mom_UPFContr", "mother_atopy_1y",
"no2.preg", "no2.y1", "num_siblings", "postnatal_smoke",
"Pregnancy.Tdens250", "prenatal_smoke", "rural", "furniture_densityclutter_3m",
"home_plant_3m", "floor_carpet_3m", "mould_3m_mh", "vaginal", "fall","summer", "winter",
"bfhistory_m","bfhistory_m_time",
"bfhistory_f","bfhistory_f_time",
"PBscore",
"WSscore",
"BAscore","eaPBscore",
"eaWSscore" ,
"eaBAscore",
"pb_sumscore",
"ws_sumscore",
"ba_sumscore" ,
"eapb_sumscore",
"eaws_sumscore",
"eaba_sumscore",
data.map$Vars[data.map$Category=="Breastmilk vitamin"])
ses.vars <- c("MR1_latent.raw","MR1_latent.raw.1y","dad_education_highest_num","mom_education_highest_num",
"mom_education_highest_1y_num","dad_education_highest1y_num",
"income_pre_num","income_1y_num","communityladder_pre18w","communityladder_1y",
"canadaladder_pre18w","canadaladder_1y")
cate.vars.all <- unique(c(data.map$Vars[data.map$Type=="Categorical"],"vaginal","cwol","fall","summer","winter"))
cont.vars.all <- unique(data.map$Vars[data.map$Type=="Continuous"])
res.var.uses <- c("atopy5y", "asthma5y_ep", "overweight", "Tscore_abnormal")
#Clean the data to be appropriate for the models
data.withmissing$income_1y_num <- as.numeric(data.withmissing$income_1y)
data.withmissing$mom_education_highest_1y_num <- as.numeric(data.withmissing$meducationhighest_1y)
data.withmissing$dad_education_highest1y_num <- as.numeric(data.withmissing$deducationhighest_1y)
#Generate phenotype with at least one of these SES-associated health risks
data.resp.sum <- data.withmissing[,c("asthma5y_ep", "overweight","Tscore_abnormal")]
for(i in 1:ncol(data.resp.sum)){data.resp.sum[,i] <- as.numeric(as.character(data.resp.sum[,i]))}
nomissing.sum <- apply(data.resp.sum,1,sum)
missing.sum <- apply(data.resp.sum,1,function(x)sum(x, na.rm=T))
data.withmissing$phenotype_atleast1 <- ifelse(missing.sum>=1,1, ifelse(nomissing.sum==0,0, NA))
# # Not RUN again################################################################################################
# #Univariable analysis with clinical and environmental factors
# UVA.res.outcome <- NULL
# data.use <- data.withmissing
# cov.vars.use <- c(cov.vars)
# ses.vars.use <- c("MR1_latent.raw.interquartile","MR1_latent.raw.interquartile.1y",
# "dad_education_highest_num","mom_education_highest_num",
# "income_pre_num", "communityladder_pre18w", "canadaladder_pre18w")
#
# #Scale the data to get standardized coefficient
#
# data.fa.scale <- data.use[,c(cov.vars.use,ses.vars.use)[c(cov.vars.use,ses.vars.use)%in%cont.vars.all]]
# for(i in 1:ncol(data.fa.scale)){
# data.fa.scale[,i] <- as.numeric(as.character(data.fa.scale[,i]))
# }
# data.fa.scale <- scale(data.fa.scale)
# data.fa.scale <- cbind(data.use[,c("subjectnumber","site", "male","ethnicity_pc1", "ethnicity_pc2", "ethnicity_pc3",
# c(cov.vars.use,ses.vars.use)[!c(cov.vars.use,ses.vars.use)%in%cont.vars.all])],
# data.fa.scale)
#
# #Univariable analysis + Sensitivity analysis for each site / adjust genetic score
# for(site.use in c("all","edmonton", "toronto", "vancouver", "winnipeg")){#
# used.s <- subset(data.fa.scale, site==site.use)$subjectnumber
# if(!site.use%in%c("all","adjsite")){
# del.vars <- names(which(apply(subset(data.fa.scale,subjectnumber%in%used.s)[,cov.vars.use],2,sd)==0))
# }
# for(cov.var.use in cov.vars.use){
# #Set up which model to use, for continous response, use lm, for categorical response, use glm
# if(cov.var.use %in% cont.vars.all){
# func.use <- "lm"
# }else{
# func.use <- "glm"
# }
#
# #Sensitivity analysis if or not adjust for genetic PCs
# for(adj.genetic in c(T,F)){
# uva.res.mr1 <- NULL
# for(ses.var.use in ses.vars.use){
# if(site.use=="all"){
#
# if(adj.genetic==T){
#
# uva.res.mr1.each <- UVAMVA.func(data.func=subset(data.fa.scale),
# res.var=cov.var.use,digit.use=4,
# conf.vars.func=unique(c(ses.var.use,
# "ethnicity_pc1", "ethnicity_pc2", "ethnicity_pc3","site","male")),
# func.type=func.use, strata=NULL,
# ana.type="MVA", ci=T, save.model=F, na.uva="Each",scale="log")
# }else{
# uva.res.mr1.each <- UVAMVA.func(subset(data.fa.scale),
# res.va=cov.var.use,digit.use=4,
# conf.vars.func=unique(c(ses.var.use,"site","male")),
# func.type=func.use, strata=NULL,
# ana.type="MVA", ci=T, save.model=F, na.uva="Each",scale="log")
# }
#
# }else{
#
# if(adj.genetic==T){
# uva.res.mr1.each <- UVAMVA.func(subset(data.fa.scale,subjectnumber%in%used.s),
# res.va=cov.var.use,digit.use=4,
# conf.vars.func=unique(c(ses.var.use,"male",
# "ethnicity_pc1", "ethnicity_pc2", "ethnicity_pc3")),
# func.type=gsub("er","",func.use), strata=NULL,
# ana.type="MVA", ci=T, save.model=F, na.uva="Each",scale="log")
# }else{
# uva.res.mr1.each <- UVAMVA.func(subset(data.fa.scale,subjectnumber%in%used.s),
# res.va=cov.var.use,digit.use=4,
# conf.vars.func=unique(c(ses.var.use),"male"),
# func.type=gsub("er","",func.use), strata=NULL,
# ana.type="MVA", ci=T, save.model=F, na.uva="Each",scale="log")
# }
# }
#
# uva.res.mr1.each <- uva.res.mr1.each[!rownames(uva.res.mr1.each)%in%c("(Intercept)","male",
# "ethnicity_pc1", "ethnicity_pc2", "ethnicity_pc3"),]
# uva.res.mr1.each$SESVar <- ses.var.use
# uva.res.mr1 <- rbind(uva.res.mr1, uva.res.mr1.each)
# }
# colnames(uva.res.mr1) <- gsub("MVA","UVA",colnames(uva.res.mr1))
# colnames(uva.res.mr1)[colnames(uva.res.mr1)=="M"] <- "U"
# uva.res.mr1$Response <- cov.var.use
# uva.res.mr1$Cohort <- site.use
# uva.res.mr1$Adj.genetic <- adj.genetic
# UVA.res.outcome <- rbind(UVA.res.outcome, uva.res.mr1)
# }
# }
# }
#
# ###########################################
# UVA.res.outcome.cont <- NULL
# data.use <- data.withmissing
# cov.vars.use <- c(cov.vars)
# ses.vars.use <- c("MR1_latent.raw.interquartile","MR1_latent.raw.interquartile.1y",
# "dad_education_highest_num","mom_education_highest_num",
# "income_pre_num", "communityladder_pre18w", "canadaladder_pre18w")
#
# #Scale the data to get standardized coefficient
#
# data.fa.scale <- data.use[,c(cov.vars.use,ses.vars.use)]
# for(i in 1:ncol(data.fa.scale)){
# data.fa.scale[,i] <- as.numeric(as.character(data.fa.scale[,i]))
# }
# data.fa.scale <- scale(data.fa.scale)
# data.fa.scale <- cbind(data.use[,c("subjectnumber","site", "male","ethnicity_pc1", "ethnicity_pc2", "ethnicity_pc3")],
# data.fa.scale)
#
# #Univariable analysis + Sensitivity analysis for each site / adjust genetic score
# for(site.use in c("all","edmonton", "toronto", "vancouver", "winnipeg")){#
# used.s <- subset(data.fa.scale, site==site.use)$subjectnumber
# if(!site.use%in%c("all","adjsite")){
# del.vars <- names(which(apply(subset(data.fa.scale,subjectnumber%in%used.s)[,cov.vars.use],2,sd)==0))
# }
# for(cov.var.use in cov.vars.use){
# func.use <- "lm"
#
#
# #Sensitivity analysis if or not adjust for genetic PCs
# for(adj.genetic in c(T,F)){
# uva.res.mr1 <- NULL
# for(ses.var.use in ses.vars.use){
# if(site.use=="all"){
#
# if(adj.genetic==T){
#
# uva.res.mr1.each <- UVAMVA.func(data.func=subset(data.fa.scale),
# res.var=ses.var.use,digit.use=4,
# conf.vars.func=unique(c(cov.var.use,
# "ethnicity_pc1", "ethnicity_pc2", "ethnicity_pc3","site","male")),
# func.type=func.use, strata=NULL,
# ana.type="MVA", ci=T, save.model=F, na.uva="Each",scale="log")
# }else{
# uva.res.mr1.each <- UVAMVA.func(subset(data.fa.scale),
# res.va=ses.var.use,digit.use=4,
# conf.vars.func=unique(c(cov.var.use,"site","male")),
# func.type=func.use, strata=NULL,
# ana.type="MVA", ci=T, save.model=F, na.uva="Each",scale="log")
# }
#
# }else{
#
# if(adj.genetic==T){
# uva.res.mr1.each <- UVAMVA.func(subset(data.fa.scale,subjectnumber%in%used.s),
# res.va=ses.var.use,digit.use=4,
# conf.vars.func=unique(c(cov.var.use,"male",
# "ethnicity_pc1", "ethnicity_pc2", "ethnicity_pc3")),
# func.type=gsub("er","",func.use), strata=NULL,
# ana.type="MVA", ci=T, save.model=F, na.uva="Each",scale="log")
# }else{
# uva.res.mr1.each <- UVAMVA.func(subset(data.fa.scale,subjectnumber%in%used.s),
# res.va=ses.var.use,digit.use=4,
# conf.vars.func=unique(cov.var.use,"male"),
# func.type=gsub("er","",func.use), strata=NULL,
# ana.type="MVA", ci=T, save.model=F, na.uva="Each",scale="log")
# }
# }
#
# uva.res.mr1.each <- uva.res.mr1.each[!rownames(uva.res.mr1.each)%in%c("(Intercept)","male",
# "ethnicity_pc1", "ethnicity_pc2", "ethnicity_pc3"),]
# uva.res.mr1.each$SESVar <- ses.var.use
# uva.res.mr1 <- rbind(uva.res.mr1, uva.res.mr1.each)
# }
# colnames(uva.res.mr1) <- gsub("MVA","UVA",colnames(uva.res.mr1))
# colnames(uva.res.mr1)[colnames(uva.res.mr1)=="M"] <- "U"
# uva.res.mr1$Response <- cov.var.use
# uva.res.mr1$Cohort <- site.use
# uva.res.mr1$Adj.genetic <- adj.genetic
# UVA.res.outcome.cont <- rbind(UVA.res.outcome.cont, uva.res.mr1)
# }
# }
# }
#
# Response.outcome <- NULL
# for(res.var.use in c("phenotype_atleast1",res.var.uses)){
# used.vars <- c("MR1_latent.raw.interquartile","SES_EBFcompound",c(cov.vars))
# data.fa.scale <- data.withmissing[,c(cov.vars[cov.vars%in%cont.vars.all])]
# for(i in 1:ncol(data.fa.scale)){
# data.fa.scale[,i] <- as.numeric(as.character(data.fa.scale[,i]))
# }
# data.fa.scale <- scale(data.fa.scale)
# data.fa.scale <- cbind(data.withmissing[,c("subjectnumber","site", "male","ethnicity_pc1", "ethnicity_pc2", "ethnicity_pc3",
# res.var.use,used.vars[!used.vars%in%colnames(data.fa.scale)])], data.fa.scale)
# data.use <- data.fa.scale
#
# for(used.var in used.vars){
# uva.res <- UVAMVA.func(data.func=data.use,
# res.var=res.var.use,digit.use=4,
# conf.vars.func=c(used.var,"male","site"),
# func.type="glm", strata=NULL,
# ana.type="MVA", ci=T, save.model=F, na.uva="Each",scale="log")
# uva.res$Response <- res.var.use
# uva.res$Cohort <- "all"
# uva.res$Cov <- used.var
# Response.outcome <- rbind(Response.outcome,uva.res)
# }
# }
# save(UVA.res.outcome, Response.outcome, UVA.res.outcome.cont, file="Results/P2 Univariate Analysis of SES.Rdata")
load("Results/P2 Univariate Analysis of SES.Rdata")Generate SES index vs. early-life factors and sensitivity analysis
UVA.res.all <- subset(UVA.res.outcome.cont, !Variables%in%c("sitetoronto","male1","sitevancouver","sitewinnipeg"))
uva.res.ses.plot <- merge(subset(UVA.res.all,Response%in%used.vars),
data.map.use[data.map.use$Vars%in%used.vars,], by.x="Response", by.y="Vars",all.x=T)
uva.res.ses.plot$ci_low <- as.numeric(gsub("\\(","",strsplit2(uva.res.ses.plot$UVA_CI,",")[,1]))
uva.res.ses.plot$ci_high <- as.numeric(gsub("\\)","",strsplit2(uva.res.ses.plot$UVA_CI,",")[,2]))
uva.res.ses.plot$sig <- ifelse(uva.res.ses.plot$UVA_P<0.05,"sig","nosig")
uva.res.ses.plot$Direction <- ifelse(uva.res.ses.plot$UVA>0,"Positive","Negative")
uva.res.ses.plot$Category <- factor(uva.res.ses.plot$Category, levels=c("SES","Pregnancy",
"Birth",
"Postnatal",
"Home Environment",
"Neighbourhood Environment",
"Parental Diet/Health",
"Breastfeeding history",
"Breastmilk vitamin"),
ordered=T)
color.sh <- data.frame(Category=c("SES","Pregnancy","Birth", "Postnatal","Home Environment", "Neighbourhood Environment", "Parental Diet/Health","Breastfeeding history" ),
color.use=c( "#1B9E77", "#BEAED4","#E6AB02", "#BF5B17", "#386CB0", "#A6CEE3", "#F0027F","black"))
ses.plot <- ggplot(unique(subset(uva.res.ses.plot, SESVar%in%"MR1_latent.raw.interquartile" & Cohort=="all" & Adj.genetic==F)),
aes(y=UVA, x=reorder(Label,UVA,decreasing=T),fill = Category,
alpha = sig)) +
theme_bw() +
ylab("Standardized coefficient") +
geom_errorbar(aes(ymin=ci_low,ymax=ci_high), width=.2, position=position_dodge(0.5)) +
geom_point(position=position_dodge(0.5), size = 3, pch = 21,col="black") +
facet_grid(~Category,scale="free",space="free",labeller = label_wrap_gen(width=8)) +
xlab("") + ggtitle("") +
scale_shape_manual(values=seq(0,10)) +
scale_color_manual(values = c('gray', 'black'))+
geom_hline(yintercept = 0) +
scale_alpha_discrete(range = c(0.2, 1)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
scale_fill_manual(values=c("#BEAED4", "#E6AB02", "#BF5B17", "#386CB0", "#A6CEE3", "#F0027F","black","purple"))
plot(ses.plot)
Generate ChordDiagram of SES vs. early-life factors (Figure 1b)
#Transform data for chorddiagram
ddd <- unique(subset(uva.res.ses.plot, SESVar%in%c("MR1_latent.raw.interquartile",
"canadaladder_pre18w","communityladder_pre18w",
"dad_education_highest_num","income_pre_num","mom_education_highest_num") & Cohort=="all" & Adj.genetic==F))
ddd <- merge(ddd, data.map[,c("Vars","Label")], by.x="SESVar", by.y="Vars")
library(networkD3)
ddd <- subset(ddd, SESVar=="MR1_latent.raw.interquartile")
plot_df <- data.frame(region=ddd$Label.x, sum_length=1, Category=ddd$Category, n=ddd$UVA, mean_gain=ifelse(ddd$U=="*",0.8,0))
plot_df <- plot_df[order(plot_df$Category),]
plot_df$region <- factor(plot_df$region, levels=plot_df$region, ordered = T)
plt <- ggplot(plot_df) +
# Make custom panel grid
geom_hline(
aes(yintercept = y),
data.frame(y = c(0:1.1)),
color = "lightgrey"
) +
geom_col(
aes(
x = region,
y = 1.1,
),
position = "dodge2",
show.legend = TRUE,
) +
geom_col(
aes(
x = region,
y = sum_length,
fill = n
),
position = "dodge2",
show.legend = TRUE,
) +
# Add dots to represent the mean gain
geom_point(
aes(
x = region,
y = mean_gain
),
size = 3,
color = "gray12",
pch=8
) +
scale_fill_continuous_divergingx(palette = 'RdBu', mid = 0, p1=0.5,p2=0.4,p3 = .1, p4 = .5,
name="Standardized coefficient",rev = T) +
coord_polar()+ theme(axis.text.x = element_text(angle =
360/(2*pi)*rev( pi/2 + seq( pi/38, 2*pi-pi/38, len=38))))
# Make it circular!
cord.diag <- plt +
theme(
plot.caption = element_text(size = 1, hjust = .5),
# Make the background white and remove extra grid lines
panel.background = element_rect(fill = "white", color = "white"),
panel.grid = element_blank(),
panel.grid.major.x = element_blank()
)plot(cord.diag)
pdf("Figures/P2 Figure 1 correlation.pdf", width=8, height=8)
plot(cord.diag)
dev.off()## quartz_off_screen
## 2Generate sensitivity analyses of SES vs. early-life factors (Figure S1C)
#Get the order in the plot
ff.order <- c(layer_scales(ses.plot, 1, 1)$x$range$range,
layer_scales(ses.plot, 1, 2)$x$range$range,
layer_scales(ses.plot, 1, 3)$x$range$range,
layer_scales(ses.plot, 1, 4)$x$range$range,
layer_scales(ses.plot, 1, 5)$x$range$range,
layer_scales(ses.plot, 1, 6)$x$range$range)
#Sensitivity analysis##
sub.cohort <- subset(uva.res.ses.plot, SESVar%in%"MR1_latent.raw.interquartile" & Adj.genetic==F)
sub.cohort$Var <- sub.cohort$Cohort
sub.genetic <- subset(uva.res.ses.plot, SESVar%in%"MR1_latent.raw.interquartile" & Cohort=="all"& Adj.genetic==T)
sub.genetic$Var <- "Adjust Genetic"
sub.ses <- subset(uva.res.ses.plot, SESVar%in%c("canadaladder_pre18w","communityladder_pre18w",
"dad_education_highest_num","income_pre_num","mom_education_highest_num") & Cohort=="all" & Adj.genetic==F)
sub.ses$Var <- sub.ses$SESVar
sub.year <- subset(uva.res.ses.plot, SESVar%in%"MR1_latent.raw.interquartile.1y" & Cohort=="all"& Adj.genetic==F)
sub.year$Var <- "1 year"
sens.plot <- rbind(sub.cohort, sub.ses,sub.genetic,sub.year)
data.map.ses <- data.frame(SES.Vars=c("all", "1 year","Adjust Genetic",
"toronto", "vancouver", "winnipeg", "edmonton",
"income_pre_num","mom_education_highest_num", "dad_education_highest_num",
"communityladder_pre18w", "canadaladder_pre18w"),
SES.Category=c("Main", "1y","Adj",
"Subcohort study center","Subcohort study center","Subcohort study center","Subcohort study center",
"Individual SES factor","Individual SES factor","Individual SES factor","Individual SES factor","Individual SES factor"),
SES.Label=c("SES index (preg.)", "SES index (1y)",
"Adjust genetic ancestry",
"Toronto", "Vancouver", "Winnipeg", "Edmonton",
"Income","Education of mother", "Education of father",
"Social status (Community)","Social status (Canada)"),
SES.Type=c("Continuous"),
SES.Values=NA)
sens.plot <- merge(sens.plot, data.map.ses, by.x="Var", by.y="SES.Vars",all.x=T)
sens.plot$SES.Label <- factor(sens.plot$SES.Label , levels=data.map.ses$SES.Label, ordered=T)
sens.plot$SES.Category <- factor(sens.plot$SES.Category, levels=unique(data.map.ses$SES.Category), ordered=T)
sens.plot$U <- ifelse(sens.plot$UVA_P<0.05,"*","")
sens.plot$Label <- factor(sens.plot$Label, levels=ff.order, ordered=T)
sens.plot.fig <- ggplot(subset(sens.plot, Category!="Health"),
aes(x=Label, y=SES.Label)) +
geom_tile(aes(fill=UVA)) +
geom_text(aes(label = U), color = "white", size = 5,vjust=1)+
theme_bw() + ylab("") + xlab("") +
facet_grid(SES.Category ~ Category ,scales = "free",space = "free",labeller = label_wrap_gen(width=15)) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
scale_fill_continuous_divergingx(palette = 'RdBu', mid = 0, p1=0.5,p2=0.4,p3 = .1, p4 = .5,
name="Standardized coefficient",rev = T)
sens.plot.fig
pdf("Figures/P2 SES & exposomes sensitivity.pdf", height=6.5, width=15)
plot(sens.plot.fig)
dev.off()## quartz_off_screen
## 2Generate phenotypes vs. early-life factors (Figure 1D)
#################################################################################################################
#Plot for phenotype and exposomes
#################################################################################################################
response.plot <- subset(Response.outcome,
!Variables%in%c("(Intercept)", "sitetoronto", "sitevancouver", "sitewinnipeg","male1",
"ethnicity_pc1","ethnicity_pc2","ethnicity_pc3") &
Response%in%c("Tscore_abnormal","asthma5y_ep",
"overweight", "atopy5y"))
response.plot.use <- merge(subset(response.plot,Cov%in%c(used.vars,"MR1_latent.raw.interquartile")),
data.map.use[data.map.use$Vars%in%c(used.vars,"MR1_latent.raw.interquartile"),],
by.x="Cov", by.y="Vars",all.x=T)
response.plot.use$Response[response.plot.use$Response=="asthma5y_ep"] <- "Asthma"
response.plot.use$Response[response.plot.use$Response=="overweight"] <- "Overweight/obese"
response.plot.use$Response[response.plot.use$Response=="atopy5y"] <- "Atopy"
response.plot.use$Response[response.plot.use$Response=="Tscore_abnormal"] <- "Abnormal child behavior"
response.plot.use$Category <- factor(response.plot.use$Category,
levels=c("SES","Pregnancy",
"Birth",
"Postnatal",
"Home Environment",
"Neighbourhood Environment",
"Parental Diet/Health"),
ordered=T)
response.plot.use$M <- ifelse(response.plot.use$MVA_P<0.05,"*","")
response.plot.use$Response <- factor(response.plot.use$Response,
levels=c( "Abnormal child behavior","Atopy","Asthma", "Overweight/obese"),ordered=T)
response.plot.use$Label <- factor(response.plot.use$Label, levels=c("SES Index",ff.order), ordered=T)
response.plot.use.fig <- ggplot(subset(response.plot.use, Category!="Health" & Cohort%in%c("all")),
aes(y=(Response), x=Label)) +
geom_tile(aes(fill=MVA)) +
geom_text(aes(label = M), color = "white", size = 4,vjust=1) +
theme_bw() + ylab("") + xlab("") +
facet_grid(~Category ,scales = "free",space = "free",labeller = label_wrap_gen(width=10)) +
scale_fill_continuous_divergingx(palette = 'RdBu', mid = 0,p1=0.5,p2=0.4, p3 =1.0, p4 =1.0,
name="Standardized coefficient",rev = T) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
response.plot.use.fig
pdf("Figures/P2 SES & exposomes & phenotype.pdf", height=4, width=15)
plot(response.plot.use.fig)
dev.off()## quartz_off_screen
## 2Generate SES index vs. phenotype (Fig 1c)
response.plot <- subset(Response.outcome, Variables=="MR1_latent.raw.interquartile" & Cohort=="all")
response.plot$ci_low <- round(exp(as.numeric(gsub("\\(","",strsplit2(response.plot$MVA_CI,",")[,1]))),2)
response.plot$ci_high <- round(exp(as.numeric(gsub("\\)","",strsplit2(response.plot$MVA_CI,",")[,2]))),2)
response.plot$MVA <- round(exp(response.plot$MVA),2)
response.plot$sig <- ifelse(response.plot$MVA_P<0.05,"sig","nosig")
response.plot$Direction <- ifelse(response.plot$MVA>0,"Positive","Negative")
response.plot <- subset(response.plot, Response%in%c("atopy5y","asthma5y_ep","overweight","Tscore_abnormal"))
response.plot$Response[response.plot$Response=="asthma5y_ep"] <- "Asthma"
response.plot$Response[response.plot$Response=="atopy5y"] <- "Atopy"
response.plot$Response[response.plot$Response=="overweight"] <- "Overweight/obese"
response.plot$Response[response.plot$Response=="Tscore_abnormal"] <- "Abnormal child behavior"
res <- response.plot[order(response.plot$Response),]
res$Response <- factor(res$Response, levels = c("Overweight/obese", "Asthma", "Atopy", "Abnormal child behavior"),
ordered=T)
res <- res[order(res$Response), ]
p_mid <- res |>
ggplot(aes(y = fct_rev(Response))) +
theme_classic() +
geom_point(aes(x=MVA), shape=15, size=8,col="black") +
geom_linerange(aes(xmin=ci_low, xmax=ci_high)) +
geom_vline(xintercept = 1, linetype="dashed") +
labs(x="Coefficient", y="") +
coord_cartesian(ylim=c(1,nrow(res)), xlim=c(0.5, 1.5)) +
annotate("text", x = -1, y = 7.5, label = "Negatively associated with SES") +
theme(axis.line.y = element_blank(),
axis.ticks.y= element_blank(),
axis.text.y= element_blank(),
axis.title.y= element_blank())
res <- res[,c("Response","MVA","ci_low","ci_high","MVA_P","MVA_N")]
res_plot <- res |>
# round estimates and 95% CIs to 2 decimal places for journal specifications
mutate(across(
c(MVA, ci_low, ci_high),
~ str_pad(
round(.x, 3),
width = 4,
pad = "0",
side = "right")),
# add an "-" between HR estimate confidence intervals
estimate_lab = paste0(MVA, " (", ci_low, ",", ci_high, ")")) |>
# round p-values to two decimal places, except in cases where p < .001
mutate(MVA_P = case_when(
MVA_P < .001 ~ "<0.001",
round(MVA_P, 3) == .05 ~ as.character(round(MVA_P,3)),
MVA_P < .01 ~ str_pad( # if less than .01, go one more decimal place
as.character(round(MVA_P, 3)),
width = 4,
pad = "0",
side = "right"
),
TRUE ~ str_pad( # otherwise just round to 2 decimal places and pad string so that .2 reads as 0.20
as.character(round(MVA_P, 3)),
width = 4,
pad = "0",
side = "right"))) |>
# add a row of data that are actually column names which will be shown on the plot in the next step
bind_rows(
data.frame(
Response = "Phenotype",
conf.low = "",
conf.high = "",
MVA_P = "p-value",
MVA_N = "N",
estimate_lab = "Odds Ratio (95% CI)")) |>
mutate(Response = fct_rev(fct_relevel(Response, "Phenotype")))
res_plot$Response <- factor(res_plot$Response,
levels=c(
"Abnormal child behavior", "Atopy", "Asthma",
"Overweight/obese" ),ordered=T)
res_plot <- res_plot[order(res_plot$Response),]
p_left <-
res_plot |>
ggplot(aes(y = Response)) +
geom_text(aes(x = 0, label = Response), hjust = 0, fontface = "bold") +
geom_text(
aes(x = 1, label = MVA_N),
hjust = 0,
fontface = ifelse(res_plot$MVA_N == "N", "bold", "plain")
) +
geom_text(
aes(x = 2, label = estimate_lab),
hjust = 0,
fontface = ifelse(res_plot$estimate_lab == "Odds Ratio (95% CI)", "bold", "plain")
) +
theme_void() +
coord_cartesian(xlim = c(0, 4))
p_right <-
res_plot |>
ggplot() +
geom_text(
aes(x = 0, y = Response, label = MVA_P),
hjust = 0,
fontface = ifelse(res_plot$MVA_P == "p-value", "bold", "plain")
) +
theme_void()
layout <- c(
patchwork::area(t = 0, l = 0, b = 30, r = 8),
patchwork::area(t = 6, l = 8, b = 30, r = 18),
patchwork::area(t = 0, l = 18, b = 30, r = 20)
)
# final plot arrangement
p_left + p_mid + p_right + plot_layout(design = layout)
pdf("Figures/P2 SES & Phenotype.pdf", height=6, width=12)
p_left + p_mid + p_right + plot_layout(design = layout)
dev.off()## quartz_off_screen
## 2Generate SES index + BF vs. phenotype (Fig 3C)
response.plot <- subset(Response.outcome, Cov=="SES_EBFcompound" & Cohort=="all")
response.plot$ci_low <- round(exp(as.numeric(gsub("\\(","",strsplit2(response.plot$MVA_CI,",")[,1]))),2)
response.plot$ci_high <- round(exp(as.numeric(gsub("\\)","",strsplit2(response.plot$MVA_CI,",")[,2]))),2)
response.plot$MVA <- round(exp(response.plot$MVA),2)
response.plot$sig <- ifelse(response.plot$MVA_P<0.05,"sig","nosig")
response.plot$Direction <- ifelse(response.plot$MVA>0,"Positive","Negative")
response.plot <- subset(response.plot, Response%in%c("atopy5y","asthma5y_ep","overweight","Tscore_abnormal","phenotype_atleast1"))
response.plot$Response[response.plot$Response=="phenotype_atleast1"] <- "At least one health risk"
response.plot$Response[response.plot$Response=="asthma5y_ep"] <- "Asthma"
response.plot$Response[response.plot$Response=="atopy5y"] <- "Atopy"
response.plot$Response[response.plot$Response=="overweight"] <- "Overweight/obese"
response.plot$Response[response.plot$Response=="Tscore_abnormal"] <- "Abnormal child behavior"
res <- response.plot[order(response.plot$Response),]
res$Response <- factor(res$Response, levels = c( "At least one health risk", "Overweight/obese", "Asthma", "Atopy", "Abnormal child behavior"),
ordered=T)
res <- res[order(res$Response), ]
res$Variables[res$Variables=="SES_EBFcompound2.Low SES w/ EBF to 6m"] <- "Low SES w/ EBF to 6m"
res$Variables[res$Variables=="SES_EBFcompound3.High SES w/o EBF to 6m"] <- "High SES w/o EBF to 6m"
res$Variables[res$Variables== "SES_EBFcompound4.High SES w/ EBF to 6m"] <- "High SES w/ EBF to 6m"
res.new <- subset(res,Variables%in%c("Low SES w/ EBF to 6m","High SES w/o EBF to 6m","High SES w/ EBF to 6m"))
res.use <- subset(res, Response== "At least one health risk" & Variables%in%c("Low SES w/ EBF to 6m","High SES w/o EBF to 6m","High SES w/ EBF to 6m"))
N.use <- with(subset(data.withmissing, !is.na(SES_EBFcompound) & !is.na(male) & !is.na(phenotype_atleast1)), table(SES_EBFcompound,phenotype_atleast1))
N.use2 <- paste(N.use[,1],N.use[,2],sep="v")
names(N.use2) <- rownames(N.use)
res.use.add <- res.use[1,]
res.use.add$MVA <- res.use.add$MVA_CI <- res.use.add$ci_high <- res.use.add$ci_low <- 1
res.use.add$Variables <- "Low SES w/o EBF to 6m"
res.use.add$MVA_P <- 1
res.use <- rbind(res.use, res.use.add)
res.use$MVA_N[res.use$Variables=="High SES w/o EBF to 6m"] <- N.use2["3.High SES w/o EBF to 6m"]
res.use$MVA_N[res.use$Variables=="High SES w/ EBF to 6m"] <- N.use2["4.High SES w/ EBF to 6m"]
res.use$MVA_N[res.use$Variables=="Low SES w/o EBF to 6m"] <- N.use2["1.Low SES w/o EBF to 6m"]
res.use$MVA_N[res.use$Variables=="Low SES w/ EBF to 6m"] <- N.use2["2.Low SES w/ EBF to 6m"]
res.use$Variables <- factor(res.use$Variables,
levels=c("Low SES w/o EBF to 6m" ,
"Low SES w/ EBF to 6m" ,
"High SES w/o EBF to 6m",
"High SES w/ EBF to 6m"
),ordered=T)
res.use <- res.use[order(res.use$Variables),]
p_mid <- res.use |>
ggplot(aes(y = fct_rev(Variables))) +
theme_classic() +
geom_point(aes(x=MVA), shape=15, size=8,col="black") +
geom_linerange(aes(xmin=ci_low, xmax=ci_high)) +
geom_vline(xintercept = 1, linetype="dashed") +
labs(x="Coefficient", y="") +
coord_cartesian( xlim=c(0.25, 1.25)) +
theme(axis.line.y = element_blank(),
axis.ticks.y= element_blank(),
axis.text.y= element_blank(),
axis.title.y= element_blank())
res_plot <- res.use |>
# round estimates and 95% CIs to 2 decimal places for journal specifications
mutate(across(
c(MVA, ci_low, ci_high),
~ str_pad(
round(.x, 3),
width = 4,
pad = "0",
side = "right")),
# add an "-" between HR estimate confidence intervals
estimate_lab = paste0(MVA, " (", ci_low, ",", ci_high, ")")) |>
# round p-values to two decimal places, except in cases where p < .001
mutate(MVA_P = case_when(
MVA_P < .001 ~ "<0.001",
round(MVA_P, 3) == .05 ~ as.character(round(MVA_P,3)),
MVA_P < .01 ~ str_pad( # if less than .01, go one more decimal place
as.character(round(MVA_P, 3)),
width = 4,
pad = "0",
side = "right"
),
TRUE ~ str_pad( # otherwise just round to 2 decimal places and pad string so that .2 reads as 0.20
as.character(round(MVA_P, 3)),
width = 4,
pad = "0",
side = "right"))) |>
# add a row of data that are actually column names which will be shown on the plot in the next step
bind_rows(
data.frame(
Response = "Phenotype",
conf.low = "",
conf.high = "",
MVA_P = "p-value",
MVA_N = "N",
estimate_lab = "Odds Ratio (95% CI)")) |>
mutate(Response = fct_rev(fct_relevel(Response, "Phenotype")))
res_plot$Variables <- factor(res_plot$Variables,
levels=c("High SES w/ EBF to 6m",
"High SES w/o EBF to 6m",
"Low SES w/ EBF to 6m" ,
"Low SES w/o EBF to 6m" ),ordered=T)
res_plot <- res_plot[order(res_plot$Variables),]
res_plot$estimate_lab[res_plot$estimate_lab=="1 (1,1)"] <- "Ref"
p_left <-
res_plot |>
ggplot(aes(y = Variables)) +
geom_text(aes(x = 0, label = Variables), hjust = 0, fontface = "bold") +
geom_text(
aes(x = 1, label = MVA_N),
hjust = 0,
fontface = ifelse(res_plot$MVA_N == "N", "bold", "plain")
) +
geom_text(
aes(x = 2, label = estimate_lab),
hjust = 0,
fontface = ifelse(res_plot$estimate_lab == "Odds Ratio (95% CI)", "bold", "plain")
) +
theme_void() +
coord_cartesian(xlim = c(0, 4))
p_right <-
res_plot |>
ggplot() +
geom_text(
aes(x = 0, y = Variables, label = MVA_P),
hjust = 0,
fontface = ifelse(res_plot$MVA_P == "p-value", "bold", "plain")
) +
theme_void()
layout <- c(
patchwork::area(t = 0, l = 0, b = 30, r = 8),
patchwork::area(t = 6, l = 8, b = 30, r = 18),
patchwork::area(t = 0, l = 18, b = 30, r = 20)
)
# final plot arrangement
p_left + p_mid + p_right + plot_layout(design = layout)
pdf("Figures/P2 SESBF & Phenotype.pdf", height=6, width=12)
p_left + p_mid + p_right + plot_layout(design = layout)
dev.off()## quartz_off_screen
## 2Run PERMANOVA analysis (Figure 2, 3)
rm(list=ls())
library(phyloseq)
library(psych)
library(corrplot)
library("psych")
library(ggplot2)
library(car)
library(magrittr)
library(ggcorrplot)
library(missForest)
library(xlsx)
library(ggpubr)
library(lavaan)
library(limma)
library(metaMint)
library(lubridate)
library(plyr)
library(stringr)
library(dplyr)
library(cowplot)
library(ggrepel)
library(ecodist)
library(reshape2)
# work.dir <- "~/Documents/SES_Manuscript/"
work.dir <- "/Users/darlene.dai/Desktop/SES/Results/manuscript/Code/"
setwd(work.dir)
load("Results/P1 Processed Metagenomic data.RData")
#No need to run again
cov.vars <- c("MR1_latent.raw", "atbx.microbiome.new", "atbx.m.birth", "atbx.m.preg", "awayhome1h_1y" ,
"awayhome1h_3m", "bf","bw_sd", "canue_Zdweldensity_pre18w" ,
"cat_1y", "condition_GestationalDiabetes_mother_prelabour",
"dog_1y", "fasthma","father_atopy_1y",
"freq_score_basic", "gest_days", "hometype_3m","masthma","maternaldistress_perceived_1y",
"maternaldistress_perceived_pre18w", "mom_bmi_best", "mom_UPFContr", "mother_atopy_1y",
"no2.preg", "no2.y1", "num_siblings", "postnatal_smoke",
"Pregnancy.Tdens250", "prenatal_smoke", "rural", "furniture_densityclutter_3m",
"home_plant_3m", "floor_carpet_3m", "mould_3m_mh", "vaginal", "fall","summer", "winter")#"male",
#
#
# No need to run again
# ############################################################################################################
# library(vegan)
# technical.vars <- c("ProcessingPeriod" ,"exact_age")
#
# PERMANOVA.UVA <- NULL
# visit.all <- c("1 year","3 month")
# vars.use <- cov.vars
# cohort <- "all"
# for(data.type in c("Species")){
# if(data.type=="Species"){
# OTU.use <- RBNME_species_MCLR
# }
#
# for(visit in visit.all){
# if(visit%in%c("3 month", "1 year")){
# s.use <- as.character(data.div$SampleID[data.div$Visit==visit])
# s.use.subjectid <- as.character(data.div$SubjectNumber[data.div$Visit==visit])
# }
# for(missing in c("raw")){
# if(missing=="raw"){data.cli.p <- data.withmissing[data.withmissing$subjectnumber%in%s.use.subjectid,]}
# data.cli.p <- merge(data.cli.p, data.div[s.use,c("SubjectNumber","SampleID","Visit","exact_age","ProcessingPeriod")],
# by.x="subjectnumber", by.y="SubjectNumber")
# # data.cli.p <- data.cli.p[!is.na(data.cli.p$MR1_latent.raw),]
# data.cli.p$atbx.microbiome.new <- ifelse(data.cli.p$Visit=="3 month", as.numeric(as.character(data.cli.p$atbx.micro3m)), as.numeric(as.character(data.cli.p$atbx.micro1y)))
# data.cli.p$bf <- ifelse(data.cli.p$exact_age*12<data.cli.p$bf_duration_imp,1, 0)
# if(visit%in%c("3 month", "1 year")){
# rownames(data.cli.p) <- data.cli.p$subjectnumber
# }
#
# s.overlap <- data.cli.p$subjectnumber
# s.overlap <- as.character(s.overlap)
# otu.table.use <- data.frame(otu_table(OTU.use))
# # otu.table.use[otu.table.use==0] <- min(otu.table.use[otu.table.use>0])/2
# # otu.table.use <- log(otu.table.use)
# otu.table.sub <- otu.table.use[s.use,]
# rownames(otu.table.sub) <- s.use.subjectid
#
# for(var.use in vars.use){
#
# var.model.use <- c("ProcessingPeriod" ,"exact_age", var.use)
#
# full.model <- paste("otu.table.model ~ ", paste(var.model.use, collapse = "+"))
# data.cli.p.nomissing <- na.omit(data.cli.p[,c(var.model.use,"site")])
#
# s.nomissing <- as.character(rownames(data.cli.p.nomissing))
# data.cli.model <- data.cli.p[s.nomissing,]
# otu.table.model <- otu.table.sub[s.nomissing,]
# set.seed(343834)
# margin.permanova <- adonis2(as.formula(full.model),
# data = data.cli.p.nomissing,
# permutations = 999,
# method="euclidean",
# by="margin",
# strata =data.cli.p.nomissing$site)
#
# res.margin <- data.frame(Data=data.type,
# Imputation=missing,
# Visit=visit,
# Cohort=cohort,
# Var=c(var.model.use,"Residual","Total"),
# N=length(s.nomissing),
# Tes=var.use,
# R2.margin=margin.permanova$R2,
# Fvalue.margin=margin.permanova$F,
# p.margin=margin.permanova$`Pr(>F)`)
#
# res <- res.margin
#
# PERMANOVA.UVA <- rbind(PERMANOVA.UVA, res)
# save(PERMANOVA.UVA, file="Results/P3 UVA PERMANOVA Res.RData")
# }
# }
# }
# }
#
# varespec.bray.all <- NULL
# visit.all <- c("1 year","3 month")
# i <- 1
# for(data.type in c("Species" )){
# if(data.type=="Species"){
# OTU.use <- RBNME_species_MCLR
# }
# if(data.type=="META"){
# OTU.use <- RBMETA
# }
# for(visit in visit.all){
# if(visit%in%c("3 month", "1 year")){
# s.use <- as.character(data.div$SampleID[data.div$Visit==visit])
# s.use.subjectid <- as.character(data.div$SubjectNumber[data.div$Visit==visit])
# }
# for(missing in c("raw")){
# if(missing=="raw"){data.cli.p <- data.withmissing[data.withmissing$subjectnumber%in%s.use.subjectid,]}
# data.cli.p <- merge(data.cli.p, data.div[s.use,c("SubjectNumber","SampleID","exact_age","ProcessingPeriod")],
# by.x="subjectnumber", by.y="SubjectNumber")
# data.cli.p <- subset(data.cli.p, exact_age<1.5)
# if(visit%in%c("3 month", "1 year")){
# rownames(data.cli.p) <- data.cli.p$subjectnumber
# }
#
# s.overlap <- data.cli.p$subjectnumber
# s.overlap <- as.character(s.overlap)
# otu.table.use <- data.frame(otu_table(OTU.use))
# # otu.table.use[otu.table.use==0] <- min(otu.table.use[otu.table.use>0])/2
# # otu.table.use <- log(otu.table.use)
# otu.table.sub <- otu.table.use[s.use,]
# rownames(otu.table.sub) <- s.use.subjectid
# vars.use <- "MR1_latent.raw"
# full.model <- paste("otu.table.model ~ ", paste(vars.use, collapse = "+"))
# data.cli.p.nomissing <- na.omit(data.cli.p[,vars.use,drop=F])
#
# s.nomissing <- as.character(rownames(data.cli.p.nomissing))
# data.cli.model <- data.cli.p[s.nomissing,]
# otu.table.model <- otu.table.sub[rownames(data.cli.model),]
#
# set.seed(343834)
# varespec.bray <- vegan::vegdist(otu.table.model, method = "euclidean") # dissimilarity matrix using bray-curtis distance indices on the varespec dataset native to vegan
# varespec.bray.all[[i]] <- list(varespec.bray,data.cli.model)
# names(varespec.bray.all)[i] <- paste(visit,data.type,sep="_")
# i <- i +1
#
# }
# }
# }
#
#
# library(ecodist)
#
# pcoaVS.sp.3m <- ecodist::pco(varespec.bray.all$`3 month_Species`[[1]], negvals = "zero", dround = 0)
# pcoaVS.sp.1y <- ecodist::pco(varespec.bray.all$`1 year_Species`[[1]], negvals = "zero", dround = 0)
#
#
# pcoaVS.sp.3m$values[1]/sum(pcoaVS.sp.3m$values)
# pcoaVS.sp.3m$values[2]/sum(pcoaVS.sp.3m$values)
#
# pcoaVS.sp.1y$values[1]/sum(pcoaVS.sp.1y$values)
# pcoaVS.sp.1y$values[2]/sum(pcoaVS.sp.1y$values)
#
# summary(pcoaVS.sp.3m)
# data1 <- data.frame(subjectnumber=rownames(varespec.bray.all$`3 month_Species`[[2]]),
# sp.3m.PCoA1=scale(pcoaVS.sp.3m$vectors[,1]),
# sp.3m.PCoA2=scale(pcoaVS.sp.3m$vectors[,2]))
# data2 <- data.frame(subjectnumber=rownames(varespec.bray.all$`1 year_Species`[[2]]),
# sp.1y.PCoA1=scale(pcoaVS.sp.1y$vectors[,1]),
# sp.1y.PCoA2=scale(pcoaVS.sp.1y$vectors[,2]))
#
# data.pcoa <- merge(data1, data2,by="subjectnumber",all=T)
# save(data.pcoa,varespec.bray.all, file= "Results/P3 PCOA data.RData")
load("Results/P3 UVA PERMANOVA Res.RData")
load("Results/P3 PCOA data.RData")
load("Results/P5 Maaslin2 Results.RData")Create alpha diversity across SES (Figure 2A,B, Figure S2A,B)
###############################################
data.raw <- data.withmissing
data.div.3m <- subset(data.div, Visit=="3 month")
data.div.1y <- subset(data.div, Visit=="1 year")
data.div.all <- merge(data.div.3m, data.div.1y, by="SubjectNumber",all=T, suffixes = c(".3m",".1y"))
data.div.all$Shannon.x.change <- data.div.all$Shannon.x.1y-data.div.all$Shannon.x.3m
data.div.all <- merge(data.raw, data.div.all,by.x="subjectnumber", by.y="SubjectNumber")
data.div.all$bf.3m <- ifelse(data.div.all$exact_age.3m*12<data.div.all$bf_duration_imp,1, 0)
data.div.all$bf.1y <- ifelse(data.div.all$exact_age.1y*12<data.div.all$bf_duration_imp,1, 0)
data.div.all$bf_status.3m <- ifelse(data.div.all$exact_age.3m*12<data.div.all$ebf_duration,"EBF",
ifelse(data.div.all$exact_age.3m*12<data.div.all$bf_duration_imp,"BF","No BF"))
data.div.all$bf_status.1y <- ifelse(data.div.all$exact_age.1y*12<data.div.all$ebf_duration,"EBF",
ifelse(data.div.all$exact_age.1y*12<data.div.all$bf_duration_imp,"BF","No BF"))
data.div.all$cat_SES <- ifelse(data.div.all$cat_SES=="1.0-25%", "SES Q1",
ifelse(data.div.all$cat_SES=="2.25-50%", "SES Q2",
ifelse(data.div.all$cat_SES=="3.50-75%", "SES Q3","SES Q4")))
my_comparisons.use <- list(c("SES Q1","SES Q2"),c("SES Q1","SES Q3"),c("SES Q1","SES Q4"))
ses.vars.use <- c("used.vatSES Q2", "used.vatSES Q3","used.vatSES Q4")
plot.var.use <- "cat_SES"
sig.var.use <- "SES Q2"
sig.var.new.use <- "SES Q1"
var.order.use <- c( "SES Q1", "SES Q2", "SES Q3", "SES Q4")
plot.var=plot.var.use
ses.vars=ses.vars.use
my_comparisons=my_comparisons.use
sig.var=sig.var.use
sig.var.new=sig.var.new.use
var.order=var.order.use
data.div.all$used.vat <- data.div.all[,plot.var]
data.3m <- melt(with(subset(data.div.all,!is.na(used.vat) & !is.na(bf_status.3m)),
table(used.vat, bf_status.3m)))
data.3m$bf_status.3m <- factor(data.3m$bf_status.3m, levels=c("EBF","BF","No BF"))
data.3m$used.vat <- factor(data.3m$used.vat, levels=var.order,ordered=T)
# Stacked + percent
library(scales)
p1.3m <- ggplot(data.3m, aes(fill=bf_status.3m, y=value, x=used.vat)) +
geom_bar(position="fill", stat="identity") +
scale_fill_manual(values = c("#c3423f" ,"#f0a390" , "#f7d0c6")) +
theme_bw()+ theme(legend.position = "none") +
geom_text(aes(label = percent(..y../tapply(..y..,..x..,sum)[..x..],accuracy=1)),
stat = "identity",position = position_fill(vjust=0.5), color="white")
data.1y <- melt(with(subset(data.div.all,!is.na(used.vat) & !is.na(bf_status.1y)),
table(used.vat, bf_status.1y)))
data.1y$bf_status.1y <- factor(data.1y$bf_status.1y, levels=c( "EBF","BF","No BF"))
data.1y$used.vat <- factor(data.1y$used.vat, levels=var.order,ordered=T)
p1.1y <- ggplot(data.1y, aes(fill=bf_status.1y, y=value, x=used.vat)) +
geom_bar(position="fill", stat="identity") +
scale_fill_manual(values = c("#f0a390" , "#f7d0c6")) +
theme_bw()+ theme(legend.position = "none") +
geom_text(aes(label = percent(..y../tapply(..y..,..x..,sum)[..x..],accuracy=1)),
stat = "identity",position = position_fill(vjust=0.5), color="white")
dat1 <- data.div.all[,c("subjectnumber","used.vat","Shannon.x.3m")]
dat1$Visit <- "3 month"
dat2 <- data.div.all[,c("subjectnumber","used.vat","Shannon.x.1y")]
dat2$Visit <- "1 year"
dat3 <- data.div.all[,c("subjectnumber","used.vat","Shannon.x.change")]
dat3$Visit <- "Change"
colnames(dat1) <- colnames(dat2) <- colnames(dat3) <-c("subjectnumber","used.vat","Shannon","Visit")
dat.all <- rbind(dat1, dat2, dat3)
dat.all$used.vat <- factor(dat.all$used.vat, levels=var.order,ordered=T)
dat.all$Visit <- factor(dat.all$Visit, levels=c("3 month", "1 year", "Change"))
SES.masslin.use <- subset(SES.maaslin,
BFsub=="All" & Race.Adj=="none" & check.var=="MR1_latent.raw" &feature=="Shannon.x" &
Normalization=="raw" & adjbasic== "ProcessingPeriod + exact_age"&
((Visit%in%c("3 month","1 year") & value%in%c("MR1_latent.raw")) |
(Visit %in%c("longitudinal") &value%in%c("time_interaction"))) &
Data=="DIV" & Prevalence_cutoff==0.1)
add.masslin.res <- data.frame(Visit=c("3 month","1 year", "Change"),
pval=signif(SES.masslin.use$pval,digits = 2))
add.masslin.res$Visit <- factor(add.masslin.res$Visit, levels=c("3 month", "1 year", "Change"))
p2.all <- ggplot(subset(dat.all,!is.na(used.vat)), aes(x=used.vat, y=Shannon, fill=used.vat)) +
geom_boxplot() +
scale_fill_manual(values = c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e")) +
stat_compare_means(comparisons = my_comparisons,label="p.signif", vjust = 0.5,
step.increase = 0.03, tip.length = 0.01) +
facet_wrap(~Visit,scale="free_y") +
theme_bw() +
ylab("Species alpha diversity (Shannon)") +
xlab("SES Latent Factor") + theme(legend.position = "none") +
geom_text(data=add.masslin.res, aes(x = 2, y = 0, label = pval),
parse = TRUE, inherit.aes=FALSE)
plot(p2.all)
p2.3m <- ggplot(subset(data.div.all,!is.na(used.vat)), aes(used.vat, Shannon.x.3m, fill=used.vat)) +
geom_boxplot() +
theme_bw() +
scale_fill_manual(values = c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e")) +
ylab("Species alpha diversity (Shannon)") +
stat_compare_means(comparisons = my_comparisons,label="p.signif",vjust = 0.8,
step.increase = 0.03, tip.length = 0.01) +
xlab("SES Latent Factor") + theme(legend.position = "none")
p2.1y <- ggplot(subset(data.div.all,!is.na(used.vat)), aes(used.vat, Shannon.x.1y, fill=used.vat)) +
geom_boxplot() +
scale_fill_manual(values = c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e")) +
theme_bw() +
ylab("Species alpha diversity (Shannon)") +
stat_compare_means(comparisons = my_comparisons,label = "p.signif",vjust = 0.5,
step.increase = 0.03, tip.length = 0.01) +
xlab("SES Latent Factor") + theme(legend.position = "none")
data.div.all$used.vat <- relevel(factor(data.div.all$used.vat,ordered=F), ref=sig.var.new)
res1.3m <- UVAMVA.func(subset(data.div.all),
res.va="Shannon.x.3m",
conf.vars.func=unique(c("used.vat","ProcessingPeriod.3m","exact_age.3m")),
func.type="lmer", strata="site",
ana.type="MVA", ci=T, save.model=F, na.uva="Each",scale="log")
res1.3m$Visit <- "3 month"
res1.3m$bf <- "Not adjust for bf"
res1.1y <- UVAMVA.func(subset(data.div.all),
res.va="Shannon.x.1y",
conf.vars.func=unique(c("used.vat","ProcessingPeriod.1y","exact_age.1y")),
func.type="lmer", strata="site",
ana.type="MVA", ci=T, save.model=F, na.uva="Each",scale="log")
res1.1y$Visit <- "1 year"
res1.1y$bf <- "Not adjust for bf"
res2.3m <- UVAMVA.func(subset(data.div.all),
res.va="Shannon.x.3m",
conf.vars.func=unique(c("used.vat","ProcessingPeriod.3m","exact_age.3m","bf_status.3m")),
func.type="lmer", strata="site",
ana.type="MVA", ci=T, save.model=F, na.uva="Each",scale="log")
res2.3m$Visit <- "3 month"
res2.3m$bf <- "Adjust for bf"
res2.1y <- UVAMVA.func(subset(data.div.all),
res.va="Shannon.x.1y",
conf.vars.func=unique(c("used.vat","ProcessingPeriod.1y","exact_age.1y","bf_status.1y")),
func.type="lmer", strata="site",
ana.type="MVA", ci=T, save.model=F, na.uva="Each",scale="log")
res2.1y$Visit <- "1 year"
res2.1y$bf <- "Adjust for bf"
shannon.ses <- rbind(res1.3m[ses.vars,],res1.1y[ses.vars,],
res2.3m[ses.vars,],res2.1y[ses.vars,])
shannon.ses$ci_low <- as.numeric(gsub("\\(","",strsplit2(shannon.ses$MVA_CI,",")[,1]))
shannon.ses$ci_high <- as.numeric(gsub("\\)","",strsplit2(shannon.ses$MVA_CI,",")[,2]))
shannon.ses$sig <- ifelse(shannon.ses$MVA_P<0.05,"sig","nosig")
shannon.ses$bf <- factor(shannon.ses$bf, levels=c("Not adjust for bf","Adjust for bf"),
ordered=T)
shannon.ses$Variables <- gsub("used.vat","",shannon.ses$Variables)
addQ1 <- subset(shannon.ses,Variables==sig.var)
addQ1$MVA <- 0
addQ1$ci_high <- 0
addQ1$ci_low <- 0
addQ1$sig <- "sig"
addQ1$Variables <- gsub(sig.var,sig.var.new,addQ1$Variables)
shannon.ses <- rbind(shannon.ses, addQ1)
shannon.ses$Variables <- factor(shannon.ses$Variables, levels=var.order, ordered=T)
p3.3m <- ggplot(subset(shannon.ses, Visit=="3 month"),
aes(y=MVA, x=Variables,fill = bf)) +
theme_bw() +
xlab("Coefficient") +
geom_errorbar(aes(ymin=ci_low,ymax=ci_high), width=.2, position=position_dodge(0.5)) +
geom_point(position=position_dodge(0.5), size = 3, pch = 21) +
ylab("") + ggtitle("") +
xlab("Coefficient") +
scale_shape_manual(values=seq(0,10)) +
scale_color_manual(values = c('gray', 'black'))+
scale_fill_manual(values = c(
"#e76f51" ,"#f7d0c6" )) +
geom_hline(yintercept = 0) + theme(legend.position = "none")
p3.1y <- ggplot(subset(shannon.ses, Visit=="1 year"),
aes(y=MVA, x=Variables,fill = bf)) +
theme_bw() +
xlab("Coefficient") +
geom_errorbar(aes(ymin=ci_low,ymax=ci_high), width=.2, position=position_dodge(0.5)) +
geom_point(position=position_dodge(0.5), size = 3, pch = 21) +
ylab("") + ggtitle("") +
xlab("Coefficient") +
scale_shape_manual(values=seq(0,10)) +
scale_color_manual(values = c('gray', 'black'))+
scale_fill_manual(values = c(
"#e76f51" , "#f7d0c6" ))+
geom_hline(yintercept = 0) + theme(legend.position = "none")
plot1.3m <- insert_xaxis_grob(p2.3m, p3.3m, grid::unit(.2, "null"), position = "top")
plot2.3m <- insert_yaxis_grob(plot1.3m, p1.3m, grid::unit(.3, "null"), position = "right")
plot1.1y <- insert_xaxis_grob(p2.1y, p3.1y, grid::unit(.2, "null"), position = "top")
plot2.1y <- insert_yaxis_grob(plot1.1y, p1.1y, grid::unit(.3, "null"), position = "right")
ggarrange(plot2.3m, plot2.1y)
pdf("Figures/P6 Diviersity and SES errorbar more.pdf",height=6,width=8)
plot(p2.all)
ggarrange(plot2.3m, plot2.1y)
dev.off()## quartz_off_screen
## 2Create PERMANOVA analysis results (Figure 2C)
data.map.use <- data.map
PERMANOVA.all <- subset(PERMANOVA.UVA, Var%in%cov.vars[!cov.vars%in%"EBF_6m"])
PERMANOVA.all[PERMANOVA.all$Var== "no2.y1" & PERMANOVA.all$Visit=="3 month","R2.margin"] <- 0
PERMANOVA.all[PERMANOVA.all$Var== "no2.y1" & PERMANOVA.all$Visit=="3 month","p.margin"] <- 1
PERMANOVA.all[PERMANOVA.all$Var== "no2.y1" & PERMANOVA.all$Visit=="3 month","Fvalue.margin"] <- NA
PERMANOVA.all.plot <- merge(PERMANOVA.all,data.map.use ,by.x="Var", by.y="Vars",all.x=T)
PERMANOVA.all.plot$Visit <- factor(PERMANOVA.all.plot$Visit, levels=c("3 month", "1 year"), ordered=T)
PERMANOVA.all.plot$U <- ifelse(PERMANOVA.all.plot$p.margin<0.05, "+","")
PERMANOVA.all.plot$Data <- factor(PERMANOVA.all.plot$Data, levels=c( "Species","META"), ordered=T)
dd <- unique(subset(PERMANOVA.all.plot))
rank.dd <- plyr::ddply(dd, .(Label), summarise, mean.r2=mean(R2.margin))
rank.dd <- rank.dd[order(rank.dd$mean.r2,decreasing = F),]
dd$Label <- factor(dd$Label, levels=unique(rank.dd$Label), ordered=T)
dd$Significance <- ifelse(dd$p.margin<0.05,"p<0.05","Not significant")
dd$R2.margin <- dd$R2.margin*100
dd$Category <- factor(dd$Category, levels=c("SES","Pregnancy","Birth",
"Postnatal",
"Home Environment",
"Neighbourhood Environment",
"Parental Diet/Health"), ordered=T)
dd.3m <- subset(dd, Visit=="3 month")
# dd.3m[order(dd.3m$R2.margin, decreasing=T),]
dd.1y <- subset(dd, Visit=="1 year")
# dd.1y[order(dd.1y$R2.margin, decreasing=T),]
dd.sig <- subset(dd, Cohort=="all")#Var %in% subset(dd,Significance=="p<0.05")$Var &
dd.sig <- subset(dd.sig, !Var%in%c("ethnicity_pc1","ethnicity_pc2","ethnicity_pc3"))
dd.sig <- dd.sig[order(dd.sig$Category, decreasing = T),]
dd.sig.3m <- subset(dd.sig, Visit=="3 month" & !Label%in%c("Air pollution (NO2) (1y)","Reg. childcare attend. (1y)","Maternal distress (1y)"))
dd.sig.3m <- dd.sig.3m[order(dd.sig.3m$R2.margin,decreasing=T),]
dd.sig.1y <- subset(dd.sig, Visit=="1 year")
dd.sig.1y <- dd.sig.1y[order(dd.sig.1y$R2.margin,decreasing=T),]
dd.sig <- rbind(dd.sig.3m,dd.sig.1y)
dd.sig <- dd.sig[order(dd.sig$R2.margin,decreasing=T),]
dd.sig$Label <- factor(dd.sig$Label, levels=rev(unique(c(dd.sig.3m$Label,dd.sig.1y$Label))),ordered=T)
dd.sig$Rank <- NA
dd.sig[dd.sig$Cohort=="all" & dd.sig$Visit=="3 month" & dd.sig$p.margin < 0.05,"Rank"][1:10] <- 1:10
dd.sig[dd.sig$Cohort=="all" & dd.sig$Visit=="1 year" & dd.sig$p.margin < 0.05,"Rank"][1:10] <- 1:10
subset(dd.sig, Visit=="3 month" & Cohort=="all")[,c("Var","R2.margin","p.margin")]## Var R2.margin p.margin
## 11 bf 1.69667145 0.001
## 73 vaginal 0.73184450 0.001
## 61 num_siblings 0.68594803 0.001
## 56 MR1_latent.raw 0.67635765 0.001
## 47 mom_bmi_best 0.46129220 0.001
## 63 postnatal_smoke 0.42890263 0.001
## 1 atbx.m.birth 0.34880811 0.001
## 68 prenatal_smoke 0.32506315 0.001
## 58 no2.preg 0.30075755 0.001
## 16 canue_Zdweldensity_pre18w 0.28893068 0.001
## 31 freq_score_basic 0.25576959 0.001
## 13 bw_sd 0.20672382 0.001
## 21 dog_1y 0.17226327 0.006
## 65 Pregnancy.Tdens250 0.16261359 0.111
## 49 mom_UPFContr 0.16048782 0.003
## 35 gest_days 0.14402386 0.002
## 17 cat_1y 0.13754602 0.012
## 28 father_atopy_1y 0.12752391 0.063
## 51 mother_atopy_1y 0.12131095 0.021
## 39 hometype_3m 0.12043892 0.501
## 37 home_plant_3m 0.12034108 0.032
## 23 fall 0.11953464 0.008
## 33 furniture_densityclutter_3m 0.11245649 0.053
## 19 condition_GestationalDiabetes_mother_prelabour 0.11175464 0.021
## 3 atbx.m.preg 0.10356430 0.041
## 9 awayhome1h_3m 0.08871805 0.170
## 54 mould_3m_mh 0.08568846 0.223
## 30 floor_carpet_3m 0.08425865 0.350
## 5 atbx.microbiome.new 0.08344628 0.235
## 69 rural 0.08133365 0.501
## 76 winter 0.08107187 0.236
## 71 summer 0.06763193 0.513
## 46 maternaldistress_perceived_pre18w 0.06761777 0.620
## 42 masthma 0.05790631 0.739
## 25 fasthma 0.05202715 0.928subset(dd.sig, Visit=="1 year" & Cohort=="all")[,c("Var","R2.margin","p.margin")]## Var R2.margin p.margin
## 12 bf 1.27173416 0.001
## 62 num_siblings 1.14646132 0.001
## 15 canue_Zdweldensity_pre18w 0.51163605 0.001
## 57 no2.preg 0.41038032 0.001
## 60 no2.y1 0.38115426 0.001
## 55 MR1_latent.raw 0.31312209 0.002
## 74 vaginal 0.28334442 0.001
## 32 freq_score_basic 0.27319909 0.001
## 6 atbx.microbiome.new 0.22865296 0.001
## 40 hometype_3m 0.21794623 0.067
## 66 Pregnancy.Tdens250 0.21431368 0.015
## 2 atbx.m.birth 0.20667344 0.004
## 48 mom_bmi_best 0.19855170 0.002
## 14 bw_sd 0.18954784 0.001
## 50 mom_UPFContr 0.17377371 0.004
## 70 rural 0.17362647 0.021
## 64 postnatal_smoke 0.16140642 0.021
## 38 home_plant_3m 0.13514464 0.031
## 22 dog_1y 0.12203312 0.119
## 7 awayhome1h_1y 0.12116461 0.043
## 67 prenatal_smoke 0.12008531 0.083
## 29 floor_carpet_3m 0.11937341 0.078
## 53 mould_3m_mh 0.10722979 0.062
## 52 mother_atopy_1y 0.10684928 0.104
## 18 cat_1y 0.10628562 0.090
## 36 gest_days 0.09646927 0.088
## 34 furniture_densityclutter_3m 0.09152818 0.164
## 26 fasthma 0.08691249 0.287
## 45 maternaldistress_perceived_pre18w 0.08602126 0.267
## 27 father_atopy_1y 0.08235287 0.564
## 4 atbx.m.preg 0.08137320 0.212
## 43 maternaldistress_perceived_1y 0.07959277 0.373
## 41 masthma 0.07491016 0.336
## 75 winter 0.05812149 0.721
## 10 awayhome1h_3m 0.05761239 0.763
## 24 fall 0.05558092 0.794
## 72 summer 0.05543002 0.799
## 20 condition_GestationalDiabetes_mother_prelabour 0.04962701 0.877permanova.plot <- ggplot(subset(dd.sig,Cohort=="all"),
aes(x=R2.margin, y=Label,fill=Category,
color=Category, alpha = U))+
geom_bar(stat="identity") +
geom_text(aes(x=0.04,label = Rank),color="white", vjust = 0.7,fontface = "bold")+
theme_bw() +
facet_grid(~Visit, scales = "free_x", labeller = label_wrap_gen(12)) +
xlab("Beta-diversity variance explained (R2) %") + ylab("") +
scale_fill_manual(values=c( "#8ab17d", "#e9c46a","#f4a261", "#e76f51", "#2a9d8f", "#287271", "#9A4770"))+
scale_color_manual(values=c( "#8ab17d", "#e9c46a","#f4a261", "#e76f51", "#2a9d8f", "#287271", "#9A4770"))
plot(permanova.plot)
pdf("Figures/P3 UVA PERMANOVA barplot.pdf", height=8, width=12)
plot(permanova.plot)
dev.off()## quartz_off_screen
## 2Generate PCoA figure (Figure 2D)
pcoa.plot <- vector("list")
pco.i <- 1
data.alpha.all.both <- NULL
visit <- "1 year"
visit <- "3 month"
for(visit in c("1 year", "3 month")){
sig.use <- "top5"
if(sig.use=="top5"){
if(visit=="3 month"){
used.pco.vars <- unique(c(unique(c(dd.sig.3m$Var[1:10]))))
}
if(visit=="1 year"){
used.pco.vars <- unique(c(unique(c(dd.sig.1y$Var[1:10]))))
}
varespec.bray.use <- varespec.bray.all
}
if(visit=="3 month"){
pcoaVS <- ecodist::pco(varespec.bray.use$`3 month_Species`[[1]], negvals = "zero", dround = 0)
data.alpha.all <- varespec.bray.use$`3 month_Species`[[2]]
data.alpha.all$Visit <- "3 month"
}
if(visit=="1 year"){
pcoaVS <- ecodist::pco(varespec.bray.use$`1 year_Species`[[1]], negvals = "zero", dround = 0)
data.alpha.all <- varespec.bray.use$`1 year_Species`[[2]]
data.alpha.all$Visit <- "1 year"
}
data.alpha.all$PCoA1 <- pcoaVS$vectors[,1]
data.alpha.all$PCoA2 <- pcoaVS$vectors[,2]
data.alpha.all$PCoA3 <- pcoaVS$vectors[,3]
data.alpha.all$PCoA4 <- pcoaVS$vectors[,4]
data.alpha.all$atbx.microbiome <- ifelse(data.alpha.all$atbx.1y.time<data.alpha.all$exact_age*365,1,0)
data.alpha.all$atbx.microbiome.new <- ifelse(data.alpha.all$Visit=="3 month", as.numeric(as.character(data.alpha.all$atbx.micro3m)),
as.numeric(as.character(data.alpha.all$atbx.micro1y)))
data.alpha.all$bf <- ifelse(data.alpha.all$exact_age*12<data.alpha.all$bf_duration_imp,1, 0)
if(any(!used.pco.vars%in%colnames(data.alpha.all))){
data.alpha.all <- merge(data.alpha.all, data.withmissing[,c("subjectnumber",used.pco.vars[!used.pco.vars%in%colnames(data.alpha.all)])],by="subjectnumber",all.x=T)
}
arrows <- NULL
for(permanova.var in c(used.pco.vars)){
data.alpha.all.use <- na.omit(data.alpha.all[,c("PCoA1",permanova.var)])
r1 <- cor(data.alpha.all.use[,"PCoA1"], as.numeric(data.alpha.all.use[,permanova.var]), method="pearson")
data.alpha.all.use <- na.omit(data.alpha.all[,c("PCoA2",permanova.var)])
r2 <- cor(data.alpha.all.use[,"PCoA2"], as.numeric(data.alpha.all.use[,permanova.var]), method="pearson")
data.alpha.all.use <- na.omit(data.alpha.all[,c("PCoA3",permanova.var)])
r3 <- cor(data.alpha.all.use[,"PCoA3"], as.numeric(data.alpha.all.use[,permanova.var]), method="pearson")
data.alpha.all.use <- na.omit(data.alpha.all[,c("PCoA4",permanova.var)])
r4 <- cor(data.alpha.all.use[,"PCoA4"], as.numeric(data.alpha.all.use[,permanova.var]), method="pearson")
res <- data.frame(var=permanova.var, PCoA1=r1, PCoA2=r2, PCoA3=r3, PCoA4=r4)
arrows <- rbind(arrows, res)
}
arrows <- merge(arrows, unique(data.map.use), by.x="var", by.y="Vars",all.x=T)
data.alpha.all$SES.cat <- ifelse(data.alpha.all$cat_SES=="1.0-25%", "SES Q1",
ifelse(data.alpha.all$cat_SES=="2.25-50%", "SES Q2",
ifelse(data.alpha.all$cat_SES=="3.50-75%", "SES Q3","SES Q4")))
save(data.alpha.all,arrows, file=paste(visit, "data.alpha.all.RData"))
if(visit=="3 month"){
pmain1 <- ggplot(data=subset(data.alpha.all,!is.na(MR1_latent.raw)), aes(PCoA1, PCoA2))+
geom_point(aes(col=SES.cat)) +
stat_ellipse(aes(fill=SES.cat), alpha=1,type='t',size =1, geom="polygon") + ##changes shading on ellipses
theme_bw() +
theme(aspect.ratio = 1)+
scale_fill_manual(values=c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e")) +
scale_color_manual(values=c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e"))
pmain2 <- ggplot(data=subset(data.alpha.all,!is.na(MR1_latent.raw)), aes(PCoA1, PCoA2))+
# geom_point(aes(col=SES.cat)) +
stat_ellipse(aes(fill=SES.cat), type='t',size =1, geom="polygon") + ##changes shading on ellipses
theme_bw() +
theme(aspect.ratio = 1)+
geom_segment(data=arrows, aes(x=0, y=0, xend=PCoA1/0.02, yend=PCoA2/0.02),
arrow=arrow(length=unit(.2, "cm")),color="gray", size=1) +
geom_label_repel(data=arrows, aes(x=PCoA1/0.02, y=PCoA2/0.02, label=Label),
label.size = NA,
fontface = 'bold', color = 'black',
max.overlaps = getOption("ggrepel.max.overlaps", default = 100),
box.padding = 0.80, point.padding = 0.5) +
scale_fill_manual(values=c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e")) +
scale_color_manual(values=c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e"))
# Marginal densities along x axis
my_comparisons <- list(c("SES Q1","SES Q2"),c("SES Q1","SES Q3"),c("SES Q1","SES Q4"))
xdens <- axis_canvas(pmain1, axis = "x") +
geom_boxplot(data = subset(data.alpha.all,!is.na(MR1_latent.raw)), aes(x = PCoA1, fill = SES.cat),
size = 0.2) +
scale_fill_manual(values=c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e"))
# Marginal densities along y axis
# Need to set coord_flip = TRUE, if you plan to use coord_flip()
ydens <- axis_canvas(pmain1, axis = "y", coord_flip = TRUE) +
geom_boxplot(data = subset(data.alpha.all,!is.na(MR1_latent.raw)), aes(x = PCoA2, fill = SES.cat),
size = 0.2) +
scale_fill_manual(values=c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e")) +
coord_flip()
p1 <- insert_xaxis_grob(pmain2, xdens, grid::unit(.1, "null"), position = "top")
p2 <- insert_yaxis_grob(p1, ydens, grid::unit(.1, "null"), position = "right")
ggdraw(p2)
}
if(visit=="1 year"){
pmain1 <- ggplot(data=subset(data.alpha.all,!is.na(MR1_latent.raw)), aes( PCoA1,PCoA3))+
# geom_point(aes(col=SES.cat)) +
stat_ellipse(aes(fill=SES.cat),type='t',size =1, geom="polygon") + ##changes shading on ellipses
theme_bw() +
theme(aspect.ratio = 1)+
scale_fill_manual(values=c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e")) +
scale_color_manual(values=c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e"))
pmain2 <- ggplot(data=subset(data.alpha.all,!is.na(MR1_latent.raw)), aes(PCoA1, PCoA3))+
# geom_point(aes(col=SES.cat)) +
stat_ellipse(aes(fill=SES.cat),type='t',size =1, geom="polygon") + ##changes shading on ellipses
theme_bw() +
theme(aspect.ratio = 1)+
geom_segment(data=arrows, aes(x=0, y=0, xend=PCoA1/0.02, yend=PCoA3/0.02),
arrow=arrow(length=unit(.2, "cm")),color="gray", size=1) +
geom_label_repel(data=arrows, aes(x=PCoA1/0.02, y=PCoA3/0.02, label=Label),
label.size = NA,
fontface = 'bold', color = 'black',
max.overlaps = getOption("ggrepel.max.overlaps", default = 100),
box.padding = 0.80, point.padding = 0.5) +
scale_fill_manual(values=c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e")) +
scale_color_manual(values=c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e"))
# Marginal densities along x axis
my_comparisons <- list(c("SES Q1","SES Q2"),c("SES Q1","SES Q3"),c("SES Q1","SES Q4"))
xdens <- axis_canvas(pmain1, axis = "x") +
geom_boxplot(data = subset(data.alpha.all,!is.na(MR1_latent.raw)), aes(x = PCoA1, fill = SES.cat),
size = 0.2) +
scale_fill_manual(values=c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e"))
# Marginal densities along y axis
# Need to set coord_flip = TRUE, if you plan to use coord_flip()
ydens <- axis_canvas(pmain1, axis = "y", coord_flip = TRUE) +
geom_boxplot(data = subset(data.alpha.all,!is.na(MR1_latent.raw)), aes(x = PCoA3, fill = SES.cat),
size = 0.2) +
scale_fill_manual(values=c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e")) +
coord_flip()
p1 <- insert_xaxis_grob(pmain2, xdens, grid::unit(.1, "null"), position = "top")
p2 <- insert_yaxis_grob(p1, ydens, grid::unit(.1, "null"), position = "right")
}
pcoa.plot[[pco.i]] <- p2
names(pcoa.plot)[pco.i] <- visit
pco.i <- pco.i + 1
data.alpha.all$Visit <- visit
data.alpha.all.both <- rbind(data.alpha.all.both, data.alpha.all)
}
names(pcoa.plot)## [1] "1 year" "3 month"pdf(paste("Figures/P3 Visit","PCOA Results.pdf"),height = 9, width=10)
plot( pcoa.plot[["3 month"]] )
plot( pcoa.plot[["1 year"]] )
dev.off()## quartz_off_screen
## 2plot( pcoa.plot[["3 month"]] )
plot( pcoa.plot[["1 year"]] )
lm.res <- data.frame(Visit=c(rep("3 month",3),rep("1 year",3)),
variable=c(paste0("PCoA",1:3),paste0("PCoA",1:3)),
pval=c(summary(lm(PCoA1 ~ MR1_latent.raw + exact_age + ProcessingPeriod, subset(data.alpha.all.both,Visit=="3 month")))$coef[2,4],
summary(lm(PCoA2 ~ MR1_latent.raw + exact_age + ProcessingPeriod, subset(data.alpha.all.both,Visit=="3 month")))$coef[2,4],
summary(lm(PCoA3 ~ MR1_latent.raw + exact_age + ProcessingPeriod, subset(data.alpha.all.both,Visit=="3 month")))$coef[2,4],
summary(lm(PCoA1 ~ MR1_latent.raw + exact_age + ProcessingPeriod, subset(data.alpha.all.both,Visit=="1 year")))$coef[2,4],
summary(lm(PCoA2 ~ MR1_latent.raw + exact_age + ProcessingPeriod, subset(data.alpha.all.both,Visit=="1 year")))$coef[2,4],
summary(lm(PCoA3 ~ MR1_latent.raw + exact_age + ProcessingPeriod, subset(data.alpha.all.both,Visit=="1 year")))$coef[2,4]))
lm.res$label.use <- paste0("Regression p-value=",signif(lm.res$pval,digits=2))
lm.res$Visit <- factor(lm.res$Visit, levels=c("3 month", "1 year"), ordered=T)
lm.res$pval <- signif(lm.res$pval, digits=2)
pdf("Figures/P3 PCoA ggplot.pdf", height=4, width=10)
library(reshape2)
data.pcoa <- melt(data.alpha.all.both[,c("subjectnumber","Visit","MR1_latent.raw","SES.cat","PCoA1","PCoA2","PCoA3","PCoA4")],
id.vars =c("subjectnumber","Visit","MR1_latent.raw","SES.cat") )
data.pcoa$Visit <- factor(data.pcoa$Visit, levels=c("3 month", "1 year"), ordered=T)
data.pcoa$Selected <- paste(data.pcoa$Visit,data.pcoa$variable,sep="_")
data.pcoa.select <- subset(data.pcoa, Selected %in%c("3 month_PCoA1", "3 month_PCoA2","1 year_PCoA3"))
data.pcoa.select$Selected <- factor(data.pcoa.select$Selected, levels=c("3 month_PCoA1", "3 month_PCoA2","1 year_PCoA3"), ordered = T)
lm.res$Selected <- paste(lm.res$Visit,lm.res$variable,sep="_")
lm.res.select <- subset(lm.res, Selected %in%c("3 month_PCoA1", "3 month_PCoA2","1 year_PCoA3"))
lm.res.select$Selected <- factor(lm.res.select$Selected, levels=c("3 month_PCoA1", "3 month_PCoA2","1 year_PCoA3"), ordered = T)
ggplot(data = subset(data.pcoa.select,!is.na(MR1_latent.raw)), aes(y = value, fill = SES.cat, x=SES.cat),
alpha = 0.7, size = 0.2) +
geom_boxplot() +
scale_fill_manual(values=c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e")) +
stat_compare_means(comparisons = my_comparisons, label="p.signif",
step.increase = 0.03, tip.length = 0.01) +
facet_grid(~Selected, scale="free") +
theme_bw() + xlab("SES quartiles") +
geom_text(data=lm.res.select,
aes(x = 4, y = -10,
label=pval), parse = TRUE, inherit.aes=FALSE)
dev.off()## quartz_off_screen
## 2pdf("Figures/P3 PCoA ggplot all.pdf", height=10, width=16)
ggplot(data = subset(data.pcoa,!is.na(MR1_latent.raw)), aes(y = value, fill = SES.cat, x=SES.cat),
alpha = 0.7, size = 0.2) +
geom_boxplot() +
scale_fill_manual(values=c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e")) +
stat_compare_means(comparisons = my_comparisons, label="p.signif",position = position_nudge(y = 2)) +
facet_grid(Visit~variable, scale="free") +
theme_bw() + xlab("SES quartiles") +
geom_text(data=lm.res,
aes(x = 4, y = -10,
label=pval), parse = TRUE, inherit.aes=FALSE)
dev.off()## quartz_off_screen
## 2ggplot(data = subset(data.pcoa,!is.na(MR1_latent.raw)), aes(y = value, fill = SES.cat, x=SES.cat),
alpha = 0.7, size = 0.2) +
geom_boxplot() +
scale_fill_manual(values=c("#c3d7bd", "#a4c19a", "#5d836e", "#3f615e")) +
stat_compare_means(comparisons = my_comparisons, label="p.signif",position = position_nudge(y = 2)) +
facet_grid(variable~Visit, scale="free") +
theme_bw() + xlab("SES quartiles") +
geom_text(data=lm.res,
aes(x = 4, y = -10,
label=pval), parse = TRUE, inherit.aes=FALSE) 
pcoaVS.sp.3m <- ecodist::pco(varespec.bray.all$`3 month_Species`[[1]], negvals = "zero", dround = 0)
pcoaVS.sp.1y <- ecodist::pco(varespec.bray.all$`1 year_Species`[[1]], negvals = "zero", dround = 0)
pcoaVS.sp.3m$values[1]/sum(pcoaVS.sp.3m$values)## [1] 0.08243889pcoaVS.sp.3m$values[2]/sum(pcoaVS.sp.3m$values)## [1] 0.07799098pcoaVS.sp.1y$values[1]/sum(pcoaVS.sp.1y$values)## [1] 0.1196784pcoaVS.sp.1y$values[3]/sum(pcoaVS.sp.1y$values)## [1] 0.04365229Generate SEM model for SES affect PCOAs through early-life factors (Figure 2E)
# rm(list=ls())
# work.dir <- "/Users/darlene.dai/Desktop/SES/Results/manuscript/Code/"
# setwd(work.dir)
# load("Results/P0 Clinical data with SES Index.RData")
# load("Results/P1 Processed Metagenomic data.RData")
# load("Results/P3 PCOA data.RData")
# load("Results/P2 Univariate Analysis of SES.Rdata")
# source("/Users/darlene.dai/Desktop/SES/Script/P4 SEM update model.R")
# ########################################################################################################
# ses.res <- subset(UVA.res.outcome, Adj.genetic==F & Variables=="MR1_latent.raw.interquartile" &
# !Variables%in%c("sitetoronto","sitevancouver","sitewinnipeg") &
# Cohort=="all")
# varespec.bray.use <- varespec.bray.all
# pcoaVS.sp.3m <- ecodist::pco(varespec.bray.use$`3 month_Species`[[1]], negvals = "zero", dround = 0) # if negvals = 0 sets all negative eigenvalues to zero; if = "rm" corrects for negative eigenvalues using method 1 of Legendre and Anderson 1999
# pcoaVS.sp.1y <- ecodist::pco(varespec.bray.use$`1 year_Species`[[1]], negvals = "zero", dround = 0) # if negvals = 0 sets all negative eigenvalues to zero; if = "rm" corrects for negative eigenvalues using method 1 of Legendre and Anderson 1999
#
# data1 <- data.frame(subjectnumber=rownames(varespec.bray.all$`3 month_Species`[[2]]),
# sp.3m.PCoA1=scale(pcoaVS.sp.3m$vectors[,1]),
# sp.3m.PCoA2=scale(pcoaVS.sp.3m$vectors[,2]),
# sp.3m.PCoA3=scale(pcoaVS.sp.3m$vectors[,3]),
# sp.3m.PCoA4=scale(pcoaVS.sp.3m$vectors[,4]))
# data2 <- data.frame(subjectnumber=rownames(varespec.bray.all$`1 year_Species`[[2]]),
# sp.1y.PCoA1=scale(pcoaVS.sp.1y$vectors[,1]),
# sp.1y.PCoA2=scale(pcoaVS.sp.1y$vectors[,2]),
# sp.1y.PCoA3=scale(pcoaVS.sp.1y$vectors[,3]),
# sp.1y.PCoA4=scale(pcoaVS.sp.1y$vectors[,4]))
# data.pcoa <- merge(data1, data2,by="subjectnumber",all=T)
# data.pcoa <- merge(data.pcoa[,c("subjectnumber","sp.3m.PCoA1","sp.1y.PCoA1","sp.3m.PCoA2","sp.1y.PCoA2","sp.3m.PCoA3","sp.1y.PCoA3","sp.3m.PCoA4","sp.1y.PCoA4")],
# merge(subset(data.div, Visit=="3 month", select=c("SubjectNumber", "exact_age","ProcessingPeriod")),
# subset(data.div, Visit=="1 year", select=c("SubjectNumber", "exact_age","ProcessingPeriod")), suffixes = c(".3m",".1y"), by="SubjectNumber",all=T),
# by.x="subjectnumber", by.y ="SubjectNumber",all.x=T)
# outcome.vars <- c("asthma5y_ep","overweight","atopy5y", "Tscore_abnormal")
# data.sem <- merge(data.withmissing, data.pcoa, by="subjectnumber",all.x=T)
# data.sem$atbx.microbiome.new.1y <- as.numeric(as.character(data.sem$atbx.micro1y))
# data.sem$atbx.microbiome.new.3m <- as.numeric(as.character(data.sem$atbx.micro3m))
# data.sem$bf.3m <- ifelse(data.sem$exact_age.3m*12<data.sem$bf_duration_imp,1, 0)
# data.sem$bf.1y <- ifelse(data.sem$exact_age.1y*12<data.sem$bf_duration_imp,1, 0)
#
#
# cate.vars.all <- data.map$Vars[data.map$Type=="Categorical"]
#
#
# ###########################################################################
#
# res.vars.list <- list(c("sp.3m.PCoA1"),c("sp.1y.PCoA3"))
#
# cutoff.uses <- c(1.1)
# impute.uses <- c(T)
# SEM.RES.ALL <- vector("list")
# sem.i <- 1
# bf.vars.use <- c("bf")
#
# for(bf.var.use in bf.vars.use){
# for(cutoff.use in cutoff.uses){
# for(resp.vars in res.vars.list){
#
# cov.use <- unique(c(ses.res$Response[ses.res$UVA_P<cutoff.use]))
#
# used.vars <- cov.use[!cov.use%in%c("EBF_6m", "bf_duration_imp")]
# data.sem.use <- data.sem[!is.na(data.sem$MR1_latent.raw),]
# adj.main <- c("vancouver", "edmonton","toronto")
#
# m.main.all<- m.test.all <- cov.use.mediators.all <- adj.vars.all <- NULL
# for(resp.var in resp.vars){
# cov.use.mediators <- used.vars
# if(resp.var%in%c("sp.3m.PCoA1","sp.3m.PCoA2","sp.3m.PCoA3","sp.3m.PCoA4")){
# cov.use.mediators <- cov.use.mediators[!cov.use.mediators%in%c("maternaldistress_perceived_1y", "awayhome1h_1y")]
# if("atbx.1y"%in%cov.use.mediators){
# cov.use.mediators[cov.use.mediators=="atbx.1y"] <- "atbx.micro3m"
# }
#
# if(bf.var.use=="bf"){
# cov.use.mediators <- c( "bf.3m",cov.use.mediators)
# }else{
# cov.use.mediators <- c( "EBF_6m",cov.use.mediators)
# }
# adj.vars <- c(adj.main, "ProcessingPeriod.3m","exact_age.3m")
# m.main <- paste(resp.var, " ~ dir1*MR1_latent.raw +", paste(paste0("mm",resp.var,cov.use.mediators,"*",cov.use.mediators),collapse=" + "), " + ",paste(adj.vars, collapse = " + "))
# m.test <- paste0("ind_",resp.var,"_",cov.use.mediators, " := ", paste0("mr",cov.use.mediators), "*", paste0("mm",resp.var,cov.use.mediators))
# }
#
# if(resp.var%in%c("sp.1y.PCoA1","sp.1y.PCoA2","sp.1y.PCoA3","sp.1y.PCoA4")){
# if("atbx.1y"%in%cov.use.mediators){
# cov.use.mediators[cov.use.mediators=="atbx.1y"] <- "atbx.micro1y"
# }
# if(bf.var.use=="bf"){
# cov.use.mediators <- c( "bf.1y",cov.use.mediators)
# }else{
# cov.use.mediators <- c( "EBF_6m",cov.use.mediators)
# }
# adj.vars <- c(adj.main, "ProcessingPeriod.1y","exact_age.1y")
# m.main <- paste(resp.var, " ~ dir1*MR1_latent.raw +", paste(paste0("mm",resp.var,cov.use.mediators,"*",cov.use.mediators),collapse=" + "), " + ",paste(adj.vars, collapse = " + "))
# m.test <- paste0("ind_",resp.var,"_",cov.use.mediators, " := ", paste0("mr",cov.use.mediators), "*", paste0("mm",resp.var,cov.use.mediators))
# }
# add.m <- NULL
# if("bf.3m"%in%cov.use.mediators.all & "bf.1y"%in%cov.use.mediators.all){
# add.m <- c(add.m, "bf.3m ~~ bf.1y")
# }
# m.main.all <- c(m.main.all, m.main)
# m.test.all <- c(m.test.all, m.test)
# cov.use.mediators.all <- unique(c(cov.use.mediators.all, cov.use.mediators))
# adj.vars.all <- unique(c(adj.vars.all, adj.vars))
# }
# m.each <- c(paste(cov.use.mediators.all,"~ ", paste0("mr",cov.use.mediators.all), "*MR1_latent.raw"," + ",paste(adj.main, collapse = " + ") ))
# used.all.vars <- c(resp.vars, cov.use.mediators.all)
# for(j in 1:length(used.all.vars)){
# data.sem.use[,used.all.vars[j]] <- as.numeric(as.character(data.sem.use[,used.all.vars[j]] ))
# }
#
# if("sp.3m.PCoA1"%in%resp.vars | "sp.1y.PCoA3"%in%resp.vars){
# change.bf.id <- which(gsub("bf.3m ~ ","",m.each)!=m.each | gsub("bf_duration.3m ~ ","",m.each)!=m.each |
# gsub("atbx.micro3m ~ ","",m.each)!=m.each)
# if(length(change.bf.id)>0){
# m.each[change.bf.id] <- paste(m.each[change.bf.id], "+ exact_age.3m")
# }
# change.bf.id <- which(gsub("bf.1y ~ ","",m.each)!=m.each |
# gsub("bf_duration.1y ~ ","",m.each)!=m.each |
# gsub("atbx.micro1y ~ ","",m.each)!=m.each)
# if(length(change.bf.id)>0){
# m.each[change.bf.id] <- paste(m.each[change.bf.id], "+ exact_age.1y")
# }
# }
#
# model.use <- c(m.main.all, m.each, add.m, m.test.all)
# model.all <- sem(paste(model.use,sep="\n"),
# data=data.sem.use,
# ordered=used.all.vars[used.all.vars%in%cate.vars.all],
# missing="pairwise")
# summary(model.all)
# kk <- parameterEstimates(model.all,standardized = T)
# kk.use <- kk[gsub("ind_","",kk$lhs)!=kk$lhs,]
# mod_indices <- modindices(model.all, sort = TRUE, alpha=0.05)
# adj.M <- updatemodel(mod_indices,
# var.add.covs=list(c(cov.use.mediators.all)),
# model.use,
# data.sem.use,
# ordered.vars=used.all.vars[used.all.vars%in%cate.vars.all],
# missing.use="pairwise")
# adj.M.use <- adj.M
# model.use.final <- c(model.use, adj.M.use)
# model.all.final <- sem(paste(model.use.final,collapse = "\n"),
# data=data.sem.use,
# ordered=used.all.vars[used.all.vars%in%cate.vars.all],
# missing="pairwise")
#
#
# SEM.RES.ALL[[sem.i]] <- list(model.use.final, model.all.final)
# names(SEM.RES.ALL)[sem.i] <- paste(resp.vars,cutoff.use,bf.var.use,collapse = "_")
# sem.i <- sem.i + 1
# save(SEM.RES.ALL, file="Results/P2 SEM of SES on PCOAs through factors.RData")
# }
# }
# }
load("Results/P2 SEM of SES on PCOAs through factors.RData")
# used.test <- "sp.3m.PCoA1 0.05 bf_sp.3m.PCoA2 0.05 bf_sp.3m.PCoA3 0.05 bf"
# used.test <- "sp.1y.PCoA1 0.05 bf_sp.1y.PCoA2 0.05 bf_sp.1y.PCoA3 0.05 bf"
names(SEM.RES.ALL)## [1] "sp.3m.PCoA1 0.05 bf" "sp.1y.PCoA3 0.05 bf" "sp.3m.PCoA1 1.1 bf"
## [4] "sp.1y.PCoA3 1.1 bf"used.test <- "sp.3m.PCoA1 1.1 bf"
model.all.final <- SEM.RES.ALL[[used.test]][[2]]
kk <- parameterEstimates(model.all.final,standardized = T)
kk.use <- kk[gsub("ind_","",kk$lhs)!=kk$lhs,]
dd <- subset(kk.use, pvalue<0.05)
FIT <- as.data.frame(t(summary(model.all.final, fit.measures=TRUE)$fit[c("cfi","tli","srmr","rmsea")]))
FIT## cfi tli srmr rmsea
## 1 0.9937728 0.9927751 0.0797722 0.006898163dd## lhs op rhs label
## 449 ind_sp.3m.PCoA1_bf.3m := mrbf.3m*mmsp.3m.PCoA1bf.3m ind_sp.3m.PCoA1_bf.3m
## est se z pvalue ci.lower ci.upper std.lv std.all std.nox
## 449 0.08 0.019 4.146 0 0.042 0.118 0.08 0.048 0.08unique(c(subset(kk,lhs=="sp.3m.PCoA1")$rhs))## [1] "MR1_latent.raw"
## [2] "bf.3m"
## [3] "atbx.micro3m"
## [4] "atbx.m.birth"
## [5] "atbx.m.preg"
## [6] "awayhome1h_3m"
## [7] "bw_sd"
## [8] "canue_Zdweldensity_pre18w"
## [9] "cat_1y"
## [10] "condition_GestationalDiabetes_mother_prelabour"
## [11] "dog_1y"
## [12] "fasthma"
## [13] "father_atopy_1y"
## [14] "freq_score_basic"
## [15] "gest_days"
## [16] "hometype_3m"
## [17] "masthma"
## [18] "maternaldistress_perceived_pre18w"
## [19] "mom_bmi_best"
## [20] "mom_UPFContr"
## [21] "mother_atopy_1y"
## [22] "no2.preg"
## [23] "no2.y1"
## [24] "num_siblings"
## [25] "postnatal_smoke"
## [26] "Pregnancy.Tdens250"
## [27] "prenatal_smoke"
## [28] "rural"
## [29] "furniture_densityclutter_3m"
## [30] "home_plant_3m"
## [31] "floor_carpet_3m"
## [32] "mould_3m_mh"
## [33] "vaginal"
## [34] "fall"
## [35] "summer"
## [36] "winter"
## [37] "vancouver"
## [38] "edmonton"
## [39] "toronto"
## [40] "ProcessingPeriod.3m"
## [41] "exact_age.3m"
## [42] "sp.3m.PCoA1"
## [43] ""kk.use[,c("label","est","pvalue","std.all")]## label est
## 449 ind_sp.3m.PCoA1_bf.3m 0.080
## 450 ind_sp.3m.PCoA1_atbx.micro3m -0.005
## 451 ind_sp.3m.PCoA1_atbx.m.birth 0.000
## 452 ind_sp.3m.PCoA1_atbx.m.preg 0.007
## 453 ind_sp.3m.PCoA1_awayhome1h_3m 0.015
## 454 ind_sp.3m.PCoA1_bw_sd 0.001
## 455 ind_sp.3m.PCoA1_canue_Zdweldensity_pre18w -0.001
## 456 ind_sp.3m.PCoA1_cat_1y 0.003
## 457 ind_sp.3m.PCoA1_condition_GestationalDiabetes_mother_prelabour 0.084
## 458 ind_sp.3m.PCoA1_dog_1y 0.000
## 459 ind_sp.3m.PCoA1_fasthma 0.000
## 460 ind_sp.3m.PCoA1_father_atopy_1y -0.002
## 461 ind_sp.3m.PCoA1_freq_score_basic -0.001
## 462 ind_sp.3m.PCoA1_gest_days -0.013
## 463 ind_sp.3m.PCoA1_hometype_3m -0.005
## 464 ind_sp.3m.PCoA1_masthma 0.001
## 465 ind_sp.3m.PCoA1_maternaldistress_perceived_pre18w -0.009
## 466 ind_sp.3m.PCoA1_mom_bmi_best -0.031
## 467 ind_sp.3m.PCoA1_mom_UPFContr 0.000
## 468 ind_sp.3m.PCoA1_mother_atopy_1y 0.004
## 469 ind_sp.3m.PCoA1_no2.preg -0.004
## 470 ind_sp.3m.PCoA1_no2.y1 0.006
## 471 ind_sp.3m.PCoA1_num_siblings 0.000
## 472 ind_sp.3m.PCoA1_postnatal_smoke 0.024
## 473 ind_sp.3m.PCoA1_Pregnancy.Tdens250 -0.003
## 474 ind_sp.3m.PCoA1_prenatal_smoke -0.010
## 475 ind_sp.3m.PCoA1_rural 0.001
## 476 ind_sp.3m.PCoA1_furniture_densityclutter_3m 0.022
## 477 ind_sp.3m.PCoA1_home_plant_3m 0.001
## 478 ind_sp.3m.PCoA1_floor_carpet_3m 0.008
## 479 ind_sp.3m.PCoA1_mould_3m_mh -0.004
## 480 ind_sp.3m.PCoA1_vaginal 0.000
## 481 ind_sp.3m.PCoA1_fall -0.001
## 482 ind_sp.3m.PCoA1_summer -0.003
## 483 ind_sp.3m.PCoA1_winter 0.002
## pvalue std.all
## 449 0.000 0.048
## 450 0.666 -0.003
## 451 0.922 0.000
## 452 0.486 0.004
## 453 0.196 0.009
## 454 0.690 0.000
## 455 0.779 -0.001
## 456 0.743 0.002
## 457 0.073 0.050
## 458 0.973 0.000
## 459 0.832 0.000
## 460 0.811 -0.001
## 461 0.817 -0.001
## 462 0.117 -0.008
## 463 0.888 -0.003
## 464 0.769 0.000
## 465 0.726 -0.005
## 466 0.139 -0.018
## 467 0.981 0.000
## 468 0.393 0.002
## 469 0.585 -0.002
## 470 0.490 0.004
## 471 0.791 0.000
## 472 0.865 0.014
## 473 0.744 -0.002
## 474 0.927 -0.006
## 475 0.957 0.000
## 476 0.391 0.013
## 477 0.938 0.000
## 478 0.246 0.005
## 479 0.738 -0.002
## 480 0.910 0.000
## 481 0.706 -0.001
## 482 0.521 -0.002
## 483 0.636 0.001kk[kk$lhs=="sp.3m.PCoA1" & kk$pvalue<0.05,c("rhs","std.all","pvalue")]## rhs std.all pvalue
## 2 bf.3m 0.270 0.000
## 5 atbx.m.preg -0.099 0.042
## 6 awayhome1h_3m -0.101 0.038
## 10 condition_GestationalDiabetes_mother_prelabour -0.260 0.026
## 14 freq_score_basic -0.079 0.038
## 31 floor_carpet_3m -0.092 0.046
## 41 exact_age.3m -0.107 0.000
## 327 sp.3m.PCoA1 0.771 0.000kk[kk$rhs=="MR1_latent.raw" & kk$pvalue<0.05,c("lhs","std.all","pvalue")]## lhs std.all pvalue
## 42 bf.3m 0.177 0.000
## 76 condition_GestationalDiabetes_mother_prelabour -0.193 0.004
## 96 gest_days 0.101 0.001
## 100 hometype_3m -0.240 0.000
## 108 maternaldistress_perceived_pre18w -0.283 0.000
## 112 mom_bmi_best -0.233 0.000
## 116 mom_UPFContr -0.151 0.000
## 136 postnatal_smoke -0.387 0.000
## 140 Pregnancy.Tdens250 0.112 0.000
## 144 prenatal_smoke -0.322 0.000
## 152 furniture_densityclutter_3m -0.205 0.000
## 156 home_plant_3m 0.106 0.008
## 164 mould_3m_mh -0.115 0.021
## NA <NA> NA NAsummary(model.all.final, fit=T)## lavaan 0.6-19 ended normally after 738 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 352
##
## Used Total
## Number of observations 1221 2752
## Number of missing patterns 182
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 574.523 565.899
## Degrees of freedom 543 543
## P-value (Chi-square) 0.169 0.240
## Scaling correction factor 1.377
## Shift parameter 148.551
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 5692.122 4112.900
## Degrees of freedom 630 630
## P-value 0.000 0.000
## Scaling correction factor 1.453
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.994 0.993
## Tucker-Lewis Index (TLI) 0.993 0.992
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.007 0.006
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.012 0.011
## P-value H_0: RMSEA <= 0.050 1.000 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.080 0.080
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Regressions:
## Estimate Std.Err z-value
## sp.3m.PCoA1 ~
## (dir1) 0.048 0.090 0.535
## (mmsp.3m.PCA1b.3) 0.705 0.083 8.500
## (mmsp.3m.PCA1t.3) 0.056 0.110 0.510
## (mmsp.3m.PCA1tbx.m.b) -0.010 0.059 -0.179
## (mmsp.3m.PCA1tbx.m.p) -0.099 0.049 -2.031
## (m.3.PCA11) -0.100 0.048 -2.074
## (mmsp.3m.PCA1b_) -0.018 0.034 -0.530
## (m.3.PCA1_Z) -0.010 0.033 -0.290
## (mmsp.3m.PCA1c_1) -0.108 0.063 -1.709
## (m.3.PCA1_G) -0.254 0.114 -2.221
## (mmsp.3m.PCA1d_1) -0.014 0.062 -0.232
## (mmsp.3m.PCA1fs) -0.014 0.056 -0.256
## (mmsp.3m.PCA1f__1) -0.014 0.057 -0.241
## (mmsp.3m.PCA1f__) -0.008 0.004 -2.078
## (mmsp.3m.PCA1g_) -0.008 0.005 -1.836
## (mm.3.PCA1_3) 0.011 0.079 0.141
## (mmsp.3m.PCA1m) 0.014 0.045 0.316
## (m.3.PCA1__18) 0.003 0.008 0.351
## (mmsp.3m.PCA1m__) 0.015 0.010 1.503
## (m.3.PCA1_U) 0.000 0.005 0.024
## (mmsp.3m.PCA1m__1) 0.055 0.042 1.314
## (mm.3.PCA12.) -0.019 0.030 -0.635
## (m.3.PCA12.1) 0.035 0.040 0.866
## (mmsp.3m.PCA1n_) 0.025 0.072 0.347
## (mmsp.3m.PCA1ps_) -0.034 0.200 -0.171
## (m.3.PCA1P) -0.001 0.002 -0.328
## (mmsp.3m.PCA1pr_) 0.017 0.190 0.092
## (mmsp.3m.PCA1r) -0.008 0.148 -0.054
## (mmsp.3m.PCA1fr__3) -0.063 0.072 -0.872
## (mmsp.3m.PCA1h__3) 0.003 0.037 0.078
## (mmsp.3m.PCA1fl__3) -0.085 0.043 -1.996
## (m.3.PCA1_3_) 0.019 0.054 0.340
## (mmsp.3m.PCA1v) -0.006 0.051 -0.116
## (mmsp.3m.PCA1fl) 0.041 0.039 1.049
## (mmsp.3m.PCA1s) 0.026 0.038 0.688
## (mmsp.3m.PCA1w) -0.025 0.046 -0.536
## 0.172 0.201 0.857
## -0.008 0.172 -0.047
## -0.054 0.229 -0.234
## 0.002 0.002 0.952
## -1.129 0.300 -3.762
## bf.3m ~
## (mrb.3) 0.114 0.024 4.762
## 0.107 0.039 2.742
## 0.012 0.031 0.391
## 0.003 0.033 0.087
## -0.686 0.129 -5.309
## atbx.micro3m ~
## (mrt.3) -0.085 0.105 -0.811
## 0.047 0.168 0.282
## 0.205 0.173 1.184
## -0.057 0.193 -0.295
## 0.885 0.644 1.375
## atbx.m.birth ~
## (mrtbx.m.b) -0.010 0.084 -0.116
## 0.353 0.125 2.816
## 0.057 0.147 0.390
## 0.129 0.145 0.893
## atbx.m.preg ~
## (mrtbx.m.p) -0.070 0.095 -0.740
## -0.161 0.139 -1.162
## -0.154 0.154 -1.006
## -0.121 0.151 -0.800
## awayhome1h_3m ~
## (m1_3) -0.151 0.090 -1.667
## -0.015 0.145 -0.106
## -0.056 0.163 -0.343
## 0.134 0.156 0.856
## bw_sd ~
## (mrb_) -0.031 0.052 -0.605
## -0.012 0.078 -0.150
## -0.090 0.084 -1.078
## -0.165 0.088 -1.888
## canue_Zdweldensity_pre18w ~
## (m_Z_) 0.106 0.097 1.093
## 1.854 0.250 7.419
## -0.034 0.501 -0.069
## 2.094 0.257 8.160
## cat_1y ~
## (mrc_1) -0.026 0.078 -0.333
## -0.019 0.120 -0.160
## 0.081 0.138 0.592
## -0.081 0.139 -0.582
## condition_GestationalDiabetes_mother_prelabour ~
## (m_GD) -0.331 0.116 -2.860
## 0.446 0.204 2.187
## 0.374 0.229 1.630
## 0.369 0.237 1.555
## dog_1y ~
## (mrd_1) 0.003 0.082 0.034
## -0.541 0.122 -4.426
## -0.004 0.128 -0.034
## -0.337 0.133 -2.533
## fasthma ~
## (mrfs) -0.032 0.084 -0.378
## 0.120 0.122 0.983
## 0.154 0.149 1.032
## -0.160 0.137 -1.168
## father_atopy_1y ~
## (mrf__1) 0.148 0.080 1.859
## 0.270 0.115 2.359
## 0.206 0.143 1.439
## 0.385 0.126 3.066
## freq_score_basic ~
## (mrf__) 0.124 0.536 0.232
## -3.019 0.817 -3.696
## 0.615 0.936 0.657
## 1.722 0.842 2.044
## gest_days ~
## (mrg_) 1.571 0.494 3.180
## -2.301 0.726 -3.170
## -1.565 0.810 -1.931
## -1.573 0.861 -1.827
## hometype_3m ~
## (mr_3) -0.476 0.075 -6.314
## 1.560 0.122 12.827
## 0.036 0.152 0.238
## 1.052 0.134 7.839
## masthma ~
## (mrms) 0.055 0.075 0.734
## -0.166 0.116 -1.424
## 0.078 0.126 0.618
## 0.132 0.123 1.072
## maternaldistress_perceived_pre18w ~
## (m__18) -2.957 0.320 -9.230
## 0.521 0.502 1.039
## 0.721 0.595 1.212
## 1.169 0.534 2.188
## mom_bmi_best ~
## (mrm__) -1.977 0.257 -7.690
## -1.008 0.440 -2.291
## 0.385 0.437 0.881
## -0.204 0.460 -0.443
## mom_UPFContr ~
## (m_UP) -2.644 0.516 -5.125
## -2.742 0.851 -3.221
## -1.780 0.915 -1.945
## -0.295 0.923 -0.320
## mother_atopy_1y ~
## (mrm__1) 0.076 0.067 1.122
## 0.434 0.104 4.168
## 0.437 0.117 3.732
## 0.488 0.113 4.326
## no2.preg ~
## (mr2.) 0.212 0.194 1.096
## 7.414 0.382 19.411
## 7.496 0.397 18.897
## 12.600 0.377 33.407
## no2.y1 ~
## (m2.1) 0.183 0.162 1.126
## 5.705 0.320 17.853
## 6.714 0.333 20.170
## 11.151 0.310 35.951
## num_siblings ~
## (mrn_) -0.016 0.039 -0.416
## -0.209 0.066 -3.158
## -0.120 0.070 -1.715
## -0.359 0.067 -5.326
## postnatal_smoke ~
## (mrps_) -0.711 0.085 -8.374
## -0.191 0.127 -1.505
## 0.036 0.134 0.265
## 0.002 0.142 0.011
## Pregnancy.Tdens250 ~
## (mP.T) 4.034 0.991 4.069
## 1.689 2.535 0.666
## -7.050 2.966 -2.377
## 30.082 2.104 14.300
## prenatal_smoke ~
## (mrpr_) -0.587 0.078 -7.487
## -0.400 0.128 -3.113
## -0.078 0.133 -0.585
## -0.154 0.133 -1.159
## rural ~
## (mrrr) -0.077 0.138 -0.560
## -1.601 0.355 -4.507
## -0.879 0.210 -4.192
## -1.113 0.200 -5.560
## furniture_densityclutter_3m ~
## (mrfr__3) -0.357 0.077 -4.619
## -0.287 0.123 -2.326
## -0.241 0.136 -1.766
## -0.216 0.139 -1.561
## home_plant_3m ~
## (mrh__3) 0.184 0.069 2.661
## 0.531 0.107 4.952
## 0.206 0.117 1.764
## 0.088 0.113 0.776
## floor_carpet_3m ~
## (mrfl__3) -0.099 0.069 -1.433
## -0.004 0.104 -0.040
## 0.462 0.125 3.708
## -0.669 0.115 -5.802
## mould_3m_mh ~
## (m_3_) -0.201 0.087 -2.307
## 0.273 0.133 2.060
## -0.381 0.194 -1.961
## 0.504 0.147 3.440
## vaginal ~
## (mrvg) -0.035 0.074 -0.477
## -0.140 0.113 -1.235
## -0.051 0.127 -0.400
## -0.290 0.121 -2.401
## fall ~
## (mrfl) -0.031 0.076 -0.404
## 0.150 0.112 1.340
## -0.004 0.128 -0.034
## -0.205 0.128 -1.603
## summer ~
## (mrsm) -0.125 0.070 -1.770
## 0.245 0.109 2.251
## -0.026 0.133 -0.198
## -0.140 0.118 -1.185
## winter ~
## (mrwn) -0.078 0.074 -1.058
## -0.040 0.118 -0.335
## 0.189 0.127 1.481
## 0.308 0.124 2.493
## P(>|z|)
##
## 0.593
## 0.000
## 0.610
## 0.858
## 0.042
## 0.038
## 0.596
## 0.772
## 0.087
## 0.026
## 0.817
## 0.798
## 0.809
## 0.038
## 0.066
## 0.888
## 0.752
## 0.726
## 0.133
## 0.981
## 0.189
## 0.526
## 0.387
## 0.728
## 0.865
## 0.743
## 0.927
## 0.957
## 0.383
## 0.938
## 0.046
## 0.734
## 0.908
## 0.294
## 0.491
## 0.592
## 0.392
## 0.962
## 0.815
## 0.341
## 0.000
##
## 0.000
## 0.006
## 0.695
## 0.931
## 0.000
##
## 0.417
## 0.778
## 0.236
## 0.768
## 0.169
##
## 0.908
## 0.005
## 0.696
## 0.372
##
## 0.459
## 0.245
## 0.315
## 0.424
##
## 0.095
## 0.916
## 0.732
## 0.392
##
## 0.545
## 0.881
## 0.281
## 0.059
##
## 0.275
## 0.000
## 0.945
## 0.000
##
## 0.739
## 0.873
## 0.554
## 0.561
##
## 0.004
## 0.029
## 0.103
## 0.120
##
## 0.973
## 0.000
## 0.973
## 0.011
##
## 0.705
## 0.325
## 0.302
## 0.243
##
## 0.063
## 0.018
## 0.150
## 0.002
##
## 0.817
## 0.000
## 0.511
## 0.041
##
## 0.001
## 0.002
## 0.053
## 0.068
##
## 0.000
## 0.000
## 0.812
## 0.000
##
## 0.463
## 0.155
## 0.537
## 0.284
##
## 0.000
## 0.299
## 0.225
## 0.029
##
## 0.000
## 0.022
## 0.378
## 0.658
##
## 0.000
## 0.001
## 0.052
## 0.749
##
## 0.262
## 0.000
## 0.000
## 0.000
##
## 0.273
## 0.000
## 0.000
## 0.000
##
## 0.260
## 0.000
## 0.000
## 0.000
##
## 0.677
## 0.002
## 0.086
## 0.000
##
## 0.000
## 0.132
## 0.791
## 0.991
##
## 0.000
## 0.505
## 0.017
## 0.000
##
## 0.000
## 0.002
## 0.559
## 0.246
##
## 0.575
## 0.000
## 0.000
## 0.000
##
## 0.000
## 0.020
## 0.077
## 0.118
##
## 0.008
## 0.000
## 0.078
## 0.438
##
## 0.152
## 0.968
## 0.000
## 0.000
##
## 0.021
## 0.039
## 0.050
## 0.001
##
## 0.633
## 0.217
## 0.689
## 0.016
##
## 0.686
## 0.180
## 0.973
## 0.109
##
## 0.077
## 0.024
## 0.843
## 0.236
##
## 0.290
## 0.738
## 0.138
## 0.013
##
## Covariances:
## Estimate Std.Err z-value
## .no2.preg ~~
## .no2.y1 6.841 0.194 35.259
## .canue_Zdweldensity_pre18w ~~
## .no2.preg 2.810 0.096 29.121
## .postnatal_smoke ~~
## .prenatal_smoke 0.807 0.029 28.169
## .canue_Zdweldensity_pre18w ~~
## .no2.y1 1.725 0.077 22.286
## .hometype_3m 0.602 0.039 15.440
## .hometype_3m ~~
## .no2.preg 1.157 0.113 10.197
## .no2.y1 ~~
## .rural -1.174 0.137 -8.559
## .hometype_3m ~~
## .no2.y1 0.770 0.095 8.132
## .fasthma ~~
## .father_atpy_1y 0.414 0.058 7.159
## .hometype_3m ~~
## .num_siblings -0.205 0.030 -6.927
## .masthma ~~
## .mother_atpy_1y 0.308 0.047 6.579
## .bw_sd ~~
## .mom_bmi_best 0.802 0.126 6.372
## .no2.preg ~~
## .rural -1.240 0.213 -5.830
## .bf.3m ~~
## .mom_bmi_best -0.261 0.051 -5.097
## .dog_1y ~~
## .freq_score_bsc 2.091 0.415 5.036
## .furniture_densityclutter_3m ~~
## .mould_3m_mh 0.308 0.061 5.032
## .bw_sd ~~
## .num_siblings 0.101 0.020 4.985
## .bf.3m ~~
## .freq_score_bsc -0.570 0.117 -4.870
## .canue_Zdweldensity_pre18w ~~
## .num_siblings -0.185 0.040 -4.673
## .no2.preg ~~
## .winter -0.585 0.125 -4.667
## .condition_GestationalDiabetes_mother_prelabour ~~
## .gest_days -2.172 0.475 -4.574
## .mom_bmi_best 1.191 0.266 4.483
## .dog_1y ~~
## .hometype_3m -0.262 0.059 -4.447
## .no2.y1 ~~
## .floor_carpt_3m -0.427 0.101 -4.246
## .hometype_3m ~~
## .frntr_dnstyc_3 0.237 0.057 4.164
## .num_siblings ~~
## .prenatal_smoke -0.114 0.027 -4.151
## .atbx.micro3m ~~
## .gest_days -1.559 0.380 -4.101
## .canue_Zdweldensity_pre18w ~~
## .rural -0.490 0.120 -4.080
## .bf.3m ~~
## .prenatal_smoke -0.064 0.016 -4.072
## .maternaldistress_perceived_pre18w ~~
## .prenatal_smoke 1.006 0.254 3.961
## .cat_1y ~~
## .frntr_dnstyc_3 0.234 0.059 3.932
## .no2.y1 ~~
## .Prgnncy.Tdn250 2.995 0.771 3.887
## .freq_score_basic ~~
## .mom_bmi_best 5.200 1.381 3.765
## .gest_days ~~
## .num_siblings -0.765 0.204 -3.753
## .freq_score_basic ~~
## .mom_UPFContr 10.338 2.772 3.730
## .no2.preg ~~
## .num_siblings -0.304 0.082 -3.691
## .freq_score_basic ~~
## .no2.y1 -2.525 0.687 -3.674
## .mom_bmi_best ~~
## .mom_UPFContr 5.161 1.408 3.665
## .mom_UPFContr ~~
## .postnatal_smok 1.684 0.461 3.651
## .no2.y1 ~~
## .frntr_dnstyc_3 0.441 0.121 3.649
## .masthma ~~
## .mom_bmi_best 0.655 0.182 3.601
## .num_siblings ~~
## .postnatal_smok -0.105 0.029 -3.599
## .no2.preg ~~
## .frntr_dnstyc_3 0.484 0.135 3.578
## .mom_bmi_best ~~
## .vaginal -0.641 0.180 -3.560
## .dog_1y ~~
## .num_siblings -0.106 0.031 -3.411
## .cat_1y ~~
## .postnatal_smok 0.210 0.062 3.368
## .fasthma ~~
## .mom_bmi_best 0.679 0.202 3.359
## .freq_score_basic ~~
## .prenatal_smoke 1.325 0.395 3.358
## .no2.preg ~~
## .floor_carpt_3m -0.369 0.113 -3.270
## .gest_days ~~
## .vaginal 1.093 0.346 3.158
## .num_siblings ~~
## .vaginal 0.093 0.030 3.129
## .cat_1y ~~
## .Prgnncy.Tdn250 2.697 0.865 3.119
## .mom_bmi_best ~~
## .home_plant_3m -0.564 0.181 -3.119
## .hometype_3m ~~
## .winter 0.167 0.053 3.116
## .maternaldistress_perceived_pre18w ~~
## .mom_bmi_best 2.659 0.855 3.111
## .freq_score_basic ~~
## .frntr_dnstyc_3 -1.203 0.393 -3.059
## .mtrnldstrs__18 4.992 1.640 3.044
## .hometype_3m ~~
## .Prgnncy.Tdn250 -1.808 0.598 -3.025
## .masthma ~~
## .mtrnldstrs__18 0.730 0.242 3.021
## .mom_bmi_best ~~
## .frntr_dnstyc_3 0.577 0.191 3.019
## .awayhome1h_3m ~~
## .mother_atpy_1y 0.181 0.061 2.984
## .no2.y1 ~~
## .num_siblings -0.207 0.070 -2.978
## .father_atopy_1y ~~
## .hometype_3m -0.170 0.058 -2.938
## .mom_UPFContr ~~
## .home_plant_3m -1.110 0.378 -2.935
## .atbx.micro3m ~~
## .cndtn_GsttnD__ 0.282 0.099 2.858
## .bf.3m ~~
## .home_plant_3m 0.040 0.014 2.853
## .maternaldistress_perceived_pre18w ~~
## .mom_UPFContr 4.606 1.623 2.838
## .freq_score_basic ~~
## .vaginal -1.054 0.379 -2.784
## .condition_GestationalDiabetes_mother_prelabour ~~
## .no2.y1 0.443 0.160 2.767
## .fasthma ~~
## .floor_carpt_3m -0.157 0.057 -2.761
## .condition_GestationalDiabetes_mother_prelabour ~~
## .mtrnldstrs__18 0.905 0.328 2.757
## .bf.3m ~~
## .postnatal_smok -0.043 0.016 -2.731
## .atbx.m.birth ~~
## .mom_bmi_best 0.600 0.220 2.729
## .bw_sd ~~
## .hometype_3m -0.097 0.036 -2.723
## .maternaldistress_perceived_pre18w ~~
## .winter 0.637 0.234 2.717
## .atbx.micro3m ~~
## .vaginal -0.184 0.068 -2.692
## .atbx.m.birth ~~
## .num_siblings -0.088 0.033 -2.673
## .canue_Zdweldensity_pre18w ~~
## .mtrnldstrs__18 -0.760 0.288 -2.643
## .cat_1y ~~
## .prenatal_smoke 0.167 0.064 2.627
## .Pregnancy.Tdens250 ~~
## .floor_carpt_3m -1.599 0.615 -2.600
## .maternaldistress_perceived_pre18w ~~
## .no2.preg -1.485 0.577 -2.575
## .canue_Zdweldensity_pre18w ~~
## .dog_1y -0.208 0.082 -2.539
## .atbx.micro3m ~~
## .frntr_dnstyc_3 0.194 0.077 2.526
## .father_atopy_1y ~~
## .mtrnldstrs__18 0.641 0.256 2.504
## .awayhome1h_3m ~~
## .masthma 0.156 0.063 2.496
## .freq_score_basic ~~
## .postnatal_smok 1.062 0.428 2.479
## .atbx.micro3m ~~
## .rural -0.324 0.131 -2.478
## .bw_sd ~~
## .cn_Zdwldnst_18 -0.109 0.044 -2.465
## .masthma ~~
## .mom_UPFContr 1.035 0.423 2.449
## .condition_GestationalDiabetes_mother_prelabour ~~
## .num_siblings -0.156 0.064 -2.446
## .atbx.m.preg ~~
## .mother_atpy_1y -0.146 0.060 -2.446
## .atbx.m.birth ~~
## .gest_days -0.881 0.361 -2.439
## .cat_1y ~~
## .floor_carpt_3m -0.132 0.054 -2.434
## .bf.3m ~~
## .awayhome1h_3m -0.040 0.016 -2.422
## .mother_atopy_1y ~~
## .Prgnncy.Tdn250 -1.559 0.647 -2.410
## .bw_sd ~~
## .fall -0.087 0.036 -2.408
## .furniture_densityclutter_3m ~~
## .floor_carpt_3m -0.130 0.054 -2.386
## .freq_score_basic ~~
## .hometype_3m -0.933 0.396 -2.356
## .bf.3m ~~
## .winter -0.034 0.015 -2.343
## .freq_score_basic ~~
## .masthma 0.859 0.376 2.283
## .atbx.m.birth ~~
## .cndtn_GsttnD__ 0.230 0.101 2.279
## .bf.3m ~~
## .num_siblings 0.017 0.008 2.228
## .cat_1y ~~
## .mould_3m_mh 0.148 0.067 2.209
## .dog_1y ~~
## .postnatal_smok 0.136 0.062 2.182
## .mom_UPFContr ~~
## .floor_carpt_3m 0.854 0.391 2.182
## .atbx.m.birth ~~
## .no2.preg -0.319 0.148 -2.160
## .atbx.m.preg ~~
## .vaginal -0.132 0.062 -2.132
## .bw_sd ~~
## .vaginal -0.072 0.034 -2.132
## .cat_1y ~~
## .cndtn_GsttnD__ -0.217 0.103 -2.105
## .bf.3m ~~
## .mom_UPFContr -0.234 0.111 -2.103
## .atbx.m.preg ~~
## .gest_days -0.782 0.372 -2.101
## .gest_days ~~
## .summer 0.772 0.369 2.093
## .postnatal_smoke ~~
## .rural 0.181 0.087 2.066
## .atbx.m.birth ~~
## .bw_sd 0.089 0.043 2.057
## .bw_sd ~~
## .Prgnncy.Tdn250 0.981 0.481 2.038
## .floor_carpt_3m -0.070 0.035 -2.020
## .mother_atpy_1y -0.066 0.033 -1.981
## .father_atopy_1y ~~
## .masthma -0.110 0.056 -1.965
## .atbx.m.preg ~~
## .mom_UPFContr 0.972 0.495 1.964
## .mom_bmi_best ~~
## .winter 0.365 0.186 1.962
## P(>|z|)
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.001
##
## 0.001
##
## 0.001
##
## 0.001
##
## 0.001
##
## 0.002
##
## 0.002
##
## 0.002
##
## 0.002
##
## 0.002
##
## 0.002
##
## 0.002
## 0.002
##
## 0.002
##
## 0.003
##
## 0.003
##
## 0.003
##
## 0.003
##
## 0.003
##
## 0.003
##
## 0.004
##
## 0.004
##
## 0.005
##
## 0.005
##
## 0.006
##
## 0.006
##
## 0.006
##
## 0.006
##
## 0.006
##
## 0.006
##
## 0.007
##
## 0.007
##
## 0.008
##
## 0.008
##
## 0.009
##
## 0.009
##
## 0.010
##
## 0.011
##
## 0.012
##
## 0.012
##
## 0.013
##
## 0.013
##
## 0.013
##
## 0.014
##
## 0.014
##
## 0.014
##
## 0.014
##
## 0.015
##
## 0.015
##
## 0.015
##
## 0.016
##
## 0.016
##
## 0.017
##
## 0.018
##
## 0.019
##
## 0.022
##
## 0.023
##
## 0.026
##
## 0.027
##
## 0.029
##
## 0.029
##
## 0.031
##
## 0.033
##
## 0.033
##
## 0.035
##
## 0.035
##
## 0.036
##
## 0.036
##
## 0.039
##
## 0.040
##
## 0.042
## 0.043
## 0.048
##
## 0.049
##
## 0.050
##
## 0.050
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .sp.3m.PCoA1 1.730 1.263 1.370 0.171
## .bf.3m 0.998 0.069 14.508 0.000
## .bw_sd 0.096 0.111 0.864 0.388
## .cn_Zdwldnst_18 0.112 0.276 0.406 0.685
## .freq_score_bsc 31.548 1.033 30.544 0.000
## .gest_days 280.875 1.066 263.484 0.000
## .mtrnldstrs__18 12.356 0.663 18.632 0.000
## .mom_bmi_best 25.569 0.586 43.665 0.000
## .mom_UPFContr 47.922 1.125 42.588 0.000
## .no2.preg 4.061 0.440 9.231 0.000
## .no2.y1 4.467 0.395 11.308 0.000
## .num_siblings 0.535 0.085 6.274 0.000
## .Prgnncy.Tdn250 19.279 2.325 8.294 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## atbx.micr3m|t1 1.738 0.213 8.169 0.000
## atbx.m.brth|t1 0.272 0.185 1.470 0.142
## atbx.m.preg|t1 0.888 0.190 4.683 0.000
## awayhm1h_3m|t1 1.510 0.198 7.643 0.000
## cat_1y|t1 0.373 0.178 2.088 0.037
## cndtn_GstD__|1 2.439 0.257 9.479 0.000
## dog_1y|t1 0.397 0.171 2.329 0.020
## fasthma|t1 0.839 0.170 4.922 0.000
## fthr_tpy_1y|t1 -0.248 0.165 -1.498 0.134
## hometype_3m|t1 0.879 0.173 5.089 0.000
## masthma|t1 0.424 0.153 2.772 0.006
## mthr_tpy_1y|t1 -0.188 0.142 -1.323 0.186
## postntl_smk|t1 0.773 0.172 4.496 0.000
## prenatl_smk|t1 0.945 0.161 5.862 0.000
## rural|t1 1.653 0.273 6.048 0.000
## frntr_dnst_3|1 0.680 0.164 4.154 0.000
## hom_plnt_3m|t1 -0.338 0.141 -2.405 0.016
## flr_crpt_3m|t1 -0.113 0.141 -0.804 0.421
## mould_3m_mh|t1 1.353 0.186 7.264 0.000
## vaginal|t1 -0.975 0.150 -6.486 0.000
## fall|t1 1.200 0.158 7.617 0.000
## summer|t1 1.222 0.154 7.937 0.000
## winter|t1 -0.035 0.138 -0.250 0.803
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .sp.3m.PCoA1 0.770 0.045 17.029 0.000
## .bf.3m 0.135 0.015 8.825 0.000
## .atbx.micro3m 1.000
## .atbx.m.birth 1.000
## .atbx.m.preg 1.000
## .awayhome1h_3m 1.000
## .bw_sd 0.864 0.034 25.618 0.000
## .cn_Zdwldnst_18 2.338 0.065 35.973 0.000
## .cat_1y 1.000
## .cndtn_GsttnD__ 1.000
## .dog_1y 1.000
## .fasthma 1.000
## .father_atpy_1y 1.000
## .freq_score_bsc 93.423 3.865 24.169 0.000
## .gest_days 85.039 3.136 27.117 0.000
## .hometype_3m 1.000
## .masthma 1.000
## .mtrnldstrs__18 36.122 1.600 22.577 0.000
## .mom_bmi_best 23.749 0.811 29.293 0.000
## .mom_UPFContr 103.946 3.992 26.036 0.000
## .mother_atpy_1y 1.000
## .no2.preg 10.115 0.258 39.181 0.000
## .no2.y1 7.711 0.194 39.820 0.000
## .num_siblings 0.582 0.024 24.161 0.000
## .postnatal_smok 1.000
## .Prgnncy.Tdn250 234.709 8.394 27.962 0.000
## .prenatal_smoke 1.000
## .rural 1.000
## .frntr_dnstyc_3 1.000
## .home_plant_3m 1.000
## .floor_carpt_3m 1.000
## .mould_3m_mh 1.000
## .vaginal 1.000
## .fall 1.000
## .summer 1.000
## .winter 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ind_.3.PCA1_.3 0.080 0.019 4.146 0.000
## ind_.3.PCA1_.3 -0.005 0.011 -0.431 0.666
## ind_.3.PCA1_.. 0.000 0.001 0.097 0.922
## ind_.3.PCA1_.. 0.007 0.010 0.697 0.486
## in_.3.PCA1_1_3 0.015 0.012 1.293 0.196
## ind_s.3.PCA1__ 0.001 0.001 0.398 0.690
## i_.3.PCA1__Z_1 -0.001 0.004 -0.280 0.779
## ind_.3.PCA1__1 0.003 0.009 0.327 0.743
## i_.3.PCA1__GD_ 0.084 0.047 1.794 0.073
## ind_.3.PCA1__1 -0.000 0.001 -0.034 0.973
## ind_sp.3.PCA1_ 0.000 0.002 0.212 0.832
## in_.3.PCA1___1 -0.002 0.008 -0.240 0.811
## ind_.3.PCA1___ -0.001 0.004 -0.231 0.817
## ind_s.3.PCA1__ -0.013 0.008 -1.569 0.117
## ind_.3.PCA1__3 -0.005 0.038 -0.141 0.888
## ind_sp.3.PCA1_ 0.001 0.003 0.294 0.769
## i_.3.PCA1___18 -0.009 0.025 -0.351 0.726
## ind_.3.PCA1___ -0.031 0.021 -1.478 0.139
## i_.3.PCA1__UPF -0.000 0.012 -0.024 0.981
## in_.3.PCA1___1 0.004 0.005 0.854 0.393
## ind_.3.PCA1_2. -0.004 0.007 -0.545 0.585
## in_.3.PCA1_2.1 0.006 0.009 0.690 0.490
## ind_s.3.PCA1__ -0.000 0.002 -0.265 0.791
## ind_s.3.PCA1__ 0.024 0.142 0.171 0.865
## i_.3.PCA1_P.T2 -0.003 0.010 -0.327 0.744
## ind_s.3.PCA1__ -0.010 0.111 -0.092 0.927
## ind_sp.3.PCA1_ 0.001 0.011 0.053 0.957
## in_.3.PCA1___3 0.022 0.026 0.858 0.391
## in_.3.PCA1___3 0.001 0.007 0.078 0.938
## in_.3.PCA1___3 0.008 0.007 1.160 0.246
## in_.3.PCA1__3_ -0.004 0.011 -0.335 0.738
## ind_sp.3.PCA1_ 0.000 0.002 0.113 0.910
## ind_sp.3.PCA1_ -0.001 0.003 -0.377 0.706
## ind_sp.3.PCA1_ -0.003 0.005 -0.642 0.521
## ind_sp.3.PCA1_ 0.002 0.004 0.473 0.636used.test <- "sp.1y.PCoA3 1.1 bf"
model.all.final <- SEM.RES.ALL[[used.test]][[2]]
kk <- parameterEstimates(model.all.final,standardized = T)
kk.use <- kk[gsub("ind_","",kk$lhs)!=kk$lhs,]
dd <- subset(kk.use, pvalue<0.05)
FIT <- as.data.frame(t(summary(model.all.final, fit.measures=TRUE)$fit[c("cfi","tli","srmr","rmsea")]))
FIT## cfi tli srmr rmsea
## 1 0.9978231 0.9974705 0.07704299 0.007759611dd## lhs op rhs label
## 478 ind_sp.1y.PCoA3_bf.1y := mrbf.1y*mmsp.1y.PCoA3bf.1y ind_sp.1y.PCoA3_bf.1y
## est se z pvalue ci.lower ci.upper std.lv std.all std.nox
## 478 -0.074 0.036 -2.073 0.038 -0.144 -0.004 -0.074 -0.044 -0.074kk[kk$lhs=="sp.1y.PCoA3" & kk$pvalue<0.05,c("rhs","std.all","pvalue")]## rhs std.all pvalue
## 2 bf.1y -0.331 0.001
## 8 bw_sd 0.089 0.028
## 351 sp.1y.PCoA3 0.840 0.000kk[kk$rhs=="MR1_latent.raw" & kk$pvalue<0.05,c("lhs","std.all","pvalue")]## lhs std.all pvalue
## 44 bf.1y 0.132 0.003
## 66 awayhome1h_3m -0.106 0.042
## 82 condition_GestationalDiabetes_mother_prelabour -0.197 0.003
## 102 gest_days 0.127 0.000
## 106 hometype_3m -0.234 0.000
## 114 maternaldistress_perceived_1y -0.168 0.000
## 118 maternaldistress_perceived_pre18w -0.286 0.000
## 122 mom_bmi_best -0.235 0.000
## 126 mom_UPFContr -0.142 0.000
## 146 postnatal_smoke -0.367 0.000
## 150 Pregnancy.Tdens250 0.105 0.000
## 154 prenatal_smoke -0.316 0.000
## 162 furniture_densityclutter_3m -0.199 0.000
## 166 home_plant_3m 0.126 0.001
## 170 floor_carpet_3m -0.084 0.027
## 174 mould_3m_mh -0.119 0.017
## NA <NA> NA NAsummary(model.all.final, fit=T)## lavaan 0.6-19 ended normally after 914 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 378
##
## Used Total
## Number of observations 1217 2752
## Number of missing patterns 228
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 649.296 636.782
## Degrees of freedom 605 605
## P-value (Chi-square) 0.103 0.180
## Scaling correction factor 1.394
## Shift parameter 170.928
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 21051.526 14425.209
## Degrees of freedom 703 703
## P-value 0.000 0.000
## Scaling correction factor 1.483
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.998 0.998
## Tucker-Lewis Index (TLI) 0.997 0.997
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.008 0.007
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.012 0.011
## P-value H_0: RMSEA <= 0.050 1.000 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.077 0.077
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Regressions:
## Estimate Std.Err z-value
## sp.1y.PCoA3 ~
## (dir1) 0.058 0.154 0.373
## (mmsp.1y.PCA3b.1) -0.677 0.202 -3.357
## (mmsp.1y.PCA3t.1) 0.069 0.058 1.191
## (mmsp.1y.PCA3tbx.m.b) -0.089 0.061 -1.464
## (mmsp.1y.PCA3tbx.m.p) 0.084 0.053 1.586
## (m.1.PCA31_1) -0.309 0.392 -0.788
## (m.1.PCA31_3) 0.094 0.136 0.687
## (mmsp.1y.PCA3b_) 0.096 0.044 2.195
## (m.1.PCA3_Z) 0.059 0.049 1.210
## (mmsp.1y.PCA3c_1) 0.006 0.065 0.096
## (m.1.PCA3_G) 0.068 0.115 0.591
## (mmsp.1y.PCA3d_1) 0.061 0.072 0.856
## (mmsp.1y.PCA3fs) 0.031 0.068 0.447
## (mmsp.1y.PCA3f__1) -0.040 0.083 -0.482
## (mmsp.1y.PCA3f__) -0.005 0.005 -1.048
## (mmsp.1y.PCA3g_) -0.001 0.004 -0.262
## (mm.1.PCA3_3) 0.029 0.140 0.204
## (mmsp.1y.PCA3m) -0.007 0.050 -0.140
## (mmsp.1y.PCA3mtr__1) 0.029 0.047 0.611
## (m.1.PCA3__18) -0.014 0.028 -0.504
## (mmsp.1y.PCA3m__) -0.003 0.009 -0.335
## (m.1.PCA3_U) -0.001 0.004 -0.273
## (mmsp.1y.PCA3mth__1) -0.016 0.046 -0.348
## (mm.1.PCA32.) -0.123 0.063 -1.945
## (m.1.PCA32.1) -0.037 0.095 -0.394
## (mmsp.1y.PCA3n_) -0.229 0.146 -1.567
## (mmsp.1y.PCA3ps_) -0.040 0.162 -0.249
## (m.1.PCA3P) -0.001 0.003 -0.179
## (mmsp.1y.PCA3pr_) 0.057 0.164 0.344
## (mmsp.1y.PCA3r) -0.262 0.300 -0.871
## (mmsp.1y.PCA3fr__3) 0.021 0.092 0.224
## (mmsp.1y.PCA3h__3) 0.045 0.050 0.895
## (mmsp.1y.PCA3fl__3) -0.045 0.075 -0.602
## (m.1.PCA3_3_) -0.015 0.052 -0.284
## (mmsp.1y.PCA3v) 0.001 0.064 0.020
## (mmsp.1y.PCA3fl) 0.031 0.040 0.782
## (mmsp.1y.PCA3s) 0.037 0.040 0.936
## (mmsp.1y.PCA3w) -0.081 0.054 -1.495
## 0.368 0.310 1.189
## 1.082 0.898 1.206
## 1.497 1.073 1.395
## 0.002 0.002 0.889
## -0.306 0.367 -0.834
## bf.1y ~
## (mrb.1) 0.109 0.037 2.979
## 0.094 0.043 2.161
## -0.094 0.048 -1.969
## -0.104 0.046 -2.258
## -0.934 0.254 -3.685
## atbx.micro1y ~
## (mrt.1) -0.061 0.082 -0.746
## -0.182 0.124 -1.469
## -0.067 0.127 -0.529
## -0.102 0.128 -0.798
## 0.924 0.434 2.125
## atbx.m.birth ~
## (mrtbx.m.b) -0.016 0.084 -0.187
## 0.289 0.124 2.334
## -0.069 0.138 -0.498
## 0.107 0.134 0.795
## atbx.m.preg ~
## (mrtbx.m.p) -0.089 0.094 -0.948
## -0.152 0.133 -1.143
## -0.239 0.146 -1.642
## -0.133 0.139 -0.960
## awayhome1h_1y ~
## (m1_1) -0.001 0.076 -0.009
## -0.136 0.115 -1.179
## 0.017 0.119 0.146
## 0.047 0.120 0.390
## awayhome1h_3m ~
## (m1_3) -0.180 0.089 -2.030
## -0.035 0.146 -0.239
## -0.008 0.147 -0.055
## 0.148 0.143 1.029
## bw_sd ~
## (mrb_) -0.054 0.052 -1.024
## -0.005 0.078 -0.061
## -0.106 0.079 -1.340
## -0.168 0.083 -2.035
## canue_Zdweldensity_pre18w ~
## (m_Z_) 0.105 0.095 1.107
## 1.800 0.239 7.536
## -0.082 0.466 -0.176
## 2.068 0.241 8.585
## cat_1y ~
## (mrc_1) -0.035 0.078 -0.450
## -0.056 0.119 -0.476
## -0.021 0.124 -0.171
## -0.232 0.136 -1.705
## condition_GestationalDiabetes_mother_prelabour ~
## (m_GD) -0.340 0.116 -2.939
## 0.392 0.194 2.026
## 0.398 0.215 1.850
## 0.404 0.214 1.894
## dog_1y ~
## (mrd_1) -0.019 0.081 -0.239
## -0.531 0.121 -4.387
## -0.021 0.117 -0.178
## -0.341 0.122 -2.787
## fasthma ~
## (mrfs) -0.045 0.085 -0.531
## 0.122 0.122 0.998
## 0.217 0.146 1.487
## -0.075 0.129 -0.581
## father_atopy_1y ~
## (mrf__1) 0.127 0.081 1.569
## 0.254 0.114 2.225
## 0.244 0.139 1.752
## 0.360 0.117 3.075
## freq_score_basic ~
## (mrf__) 0.139 0.530 0.262
## -2.670 0.817 -3.270
## 0.769 0.897 0.857
## 2.054 0.780 2.634
## gest_days ~
## (mrg_) 1.993 0.492 4.049
## -2.492 0.721 -3.458
## -2.086 0.781 -2.670
## -2.602 0.795 -3.271
## hometype_3m ~
## (mr_3) -0.463 0.076 -6.064
## 1.461 0.121 12.085
## -0.095 0.149 -0.635
## 0.900 0.129 6.990
## masthma ~
## (mrms) 0.045 0.075 0.603
## -0.187 0.114 -1.631
## -0.008 0.118 -0.069
## -0.009 0.116 -0.077
## maternaldistress_perceived_1y ~
## (mrmtr__1) -1.838 0.359 -5.119
## -0.161 0.542 -0.298
## -0.628 0.602 -1.044
## 0.556 0.552 1.007
## maternaldistress_perceived_pre18w ~
## (m__18) -2.990 0.324 -9.231
## 0.646 0.491 1.316
## 0.823 0.560 1.469
## 1.082 0.508 2.131
## mom_bmi_best ~
## (mrm__) -2.029 0.257 -7.909
## -1.106 0.433 -2.552
## 0.052 0.411 0.126
## -0.479 0.433 -1.106
## mom_UPFContr ~
## (m_UP) -2.477 0.521 -4.753
## -2.884 0.856 -3.369
## -1.487 0.866 -1.718
## -0.330 0.833 -0.396
## mother_atopy_1y ~
## (mrmth__1) 0.090 0.068 1.333
## 0.446 0.104 4.304
## 0.377 0.111 3.402
## 0.413 0.107 3.851
## no2.preg ~
## (mr2.) 0.286 0.190 1.504
## 7.302 0.367 19.907
## 7.558 0.366 20.666
## 12.450 0.353 35.237
## no2.y1 ~
## (m2.1) 0.236 0.161 1.467
## 5.639 0.320 17.642
## 6.657 0.324 20.518
## 10.945 0.306 35.763
## num_siblings ~
## (mrn_) -0.004 0.039 -0.103
## -0.198 0.064 -3.075
## -0.063 0.065 -0.967
## -0.338 0.066 -5.096
## postnatal_smoke ~
## (mrps_) -0.677 0.084 -8.059
## -0.202 0.128 -1.576
## 0.043 0.126 0.343
## -0.080 0.138 -0.579
## Pregnancy.Tdens250 ~
## (mP.T) 3.802 0.992 3.835
## 2.082 2.441 0.853
## -6.656 2.874 -2.316
## 31.443 2.020 15.567
## prenatal_smoke ~
## (mrpr_) -0.578 0.078 -7.370
## -0.404 0.127 -3.186
## -0.088 0.124 -0.714
## -0.136 0.125 -1.083
## rural ~
## (mrrr) -0.092 0.135 -0.681
## -1.557 0.356 -4.377
## -0.646 0.183 -3.529
## -1.045 0.230 -4.548
## furniture_densityclutter_3m ~
## (mrfr__3) -0.349 0.079 -4.437
## -0.282 0.121 -2.327
## -0.308 0.132 -2.338
## -0.217 0.126 -1.723
## home_plant_3m ~
## (mrh__3) 0.220 0.069 3.185
## 0.545 0.107 5.108
## 0.149 0.112 1.326
## 0.102 0.106 0.963
## floor_carpet_3m ~
## (mrfl__3) -0.155 0.070 -2.217
## 0.011 0.103 0.104
## 0.488 0.118 4.134
## -0.669 0.108 -6.189
## mould_3m_mh ~
## (m_3_) -0.210 0.088 -2.378
## 0.258 0.130 1.981
## -0.409 0.181 -2.255
## 0.480 0.140 3.431
## vaginal ~
## (mrvg) -0.020 0.075 -0.265
## -0.131 0.113 -1.157
## -0.096 0.121 -0.795
## -0.370 0.113 -3.284
## fall ~
## (mrfl) -0.038 0.076 -0.499
## 0.236 0.110 2.156
## 0.150 0.120 1.250
## 0.080 0.117 0.683
## summer ~
## (mrsm) -0.119 0.071 -1.670
## 0.202 0.108 1.873
## 0.047 0.118 0.400
## -0.108 0.117 -0.926
## winter ~
## (mrwn) -0.070 0.075 -0.935
## -0.071 0.117 -0.607
## 0.028 0.120 0.228
## 0.109 0.117 0.934
## P(>|z|)
##
## 0.709
## 0.001
## 0.234
## 0.143
## 0.113
## 0.431
## 0.492
## 0.028
## 0.226
## 0.924
## 0.555
## 0.392
## 0.655
## 0.630
## 0.295
## 0.793
## 0.838
## 0.889
## 0.541
## 0.614
## 0.738
## 0.785
## 0.728
## 0.052
## 0.694
## 0.117
## 0.803
## 0.858
## 0.731
## 0.384
## 0.822
## 0.371
## 0.547
## 0.776
## 0.984
## 0.434
## 0.350
## 0.135
## 0.234
## 0.228
## 0.163
## 0.374
## 0.404
##
## 0.003
## 0.031
## 0.049
## 0.024
## 0.000
##
## 0.456
## 0.142
## 0.597
## 0.425
## 0.034
##
## 0.852
## 0.020
## 0.618
## 0.427
##
## 0.343
## 0.253
## 0.101
## 0.337
##
## 0.993
## 0.238
## 0.884
## 0.696
##
## 0.042
## 0.811
## 0.956
## 0.304
##
## 0.306
## 0.952
## 0.180
## 0.042
##
## 0.268
## 0.000
## 0.860
## 0.000
##
## 0.653
## 0.634
## 0.865
## 0.088
##
## 0.003
## 0.043
## 0.064
## 0.058
##
## 0.811
## 0.000
## 0.859
## 0.005
##
## 0.595
## 0.318
## 0.137
## 0.561
##
## 0.117
## 0.026
## 0.080
## 0.002
##
## 0.793
## 0.001
## 0.392
## 0.008
##
## 0.000
## 0.001
## 0.008
## 0.001
##
## 0.000
## 0.000
## 0.525
## 0.000
##
## 0.546
## 0.103
## 0.945
## 0.939
##
## 0.000
## 0.766
## 0.297
## 0.314
##
## 0.000
## 0.188
## 0.142
## 0.033
##
## 0.000
## 0.011
## 0.900
## 0.269
##
## 0.000
## 0.001
## 0.086
## 0.692
##
## 0.182
## 0.000
## 0.001
## 0.000
##
## 0.133
## 0.000
## 0.000
## 0.000
##
## 0.142
## 0.000
## 0.000
## 0.000
##
## 0.918
## 0.002
## 0.333
## 0.000
##
## 0.000
## 0.115
## 0.731
## 0.563
##
## 0.000
## 0.394
## 0.021
## 0.000
##
## 0.000
## 0.001
## 0.475
## 0.279
##
## 0.496
## 0.000
## 0.000
## 0.000
##
## 0.000
## 0.020
## 0.019
## 0.085
##
## 0.001
## 0.000
## 0.185
## 0.336
##
## 0.027
## 0.917
## 0.000
## 0.000
##
## 0.017
## 0.048
## 0.024
## 0.001
##
## 0.791
## 0.247
## 0.427
## 0.001
##
## 0.618
## 0.031
## 0.211
## 0.495
##
## 0.095
## 0.061
## 0.689
## 0.355
##
## 0.350
## 0.544
## 0.819
## 0.350
##
## Covariances:
## Estimate Std.Err z-value
## .no2.y1 ~~
## .rural -2.782 0.035 -78.793
## .no2.preg ~~
## .rural -3.134 0.040 -78.321
## .awayhome1h_1y ~~
## .mtrnldstrss__1 6.359 0.145 43.790
## .no2.preg ~~
## .no2.y1 6.862 0.195 35.125
## .canue_Zdweldensity_pre18w ~~
## .no2.preg 2.774 0.093 29.933
## .postnatal_smoke ~~
## .prenatal_smoke 0.810 0.028 28.459
## .canue_Zdweldensity_pre18w ~~
## .no2.y1 1.790 0.073 24.445
## .maternaldistress_perceived_1y ~~
## .rural -3.613 0.165 -21.891
## .canue_Zdweldensity_pre18w ~~
## .hometype_3m 0.701 0.032 21.620
## .maternaldistress_perceived_1y ~~
## .mtrnldstrs__18 16.861 1.211 13.926
## .hometype_3m ~~
## .no2.preg 1.130 0.112 10.103
## .no2.y1 0.805 0.092 8.754
## .fasthma ~~
## .father_atpy_1y 0.440 0.058 7.573
## .masthma ~~
## .mother_atpy_1y 0.314 0.047 6.687
## .hometype_3m ~~
## .num_siblings -0.199 0.030 -6.637
## .bw_sd ~~
## .mom_bmi_best 0.792 0.125 6.311
## .awayhome1h_1y ~~
## .num_siblings -0.176 0.032 -5.565
## .dog_1y ~~
## .freq_score_bsc 2.170 0.411 5.283
## .furniture_densityclutter_3m ~~
## .mould_3m_mh 0.315 0.061 5.123
## .dog_1y ~~
## .hometype_3m -0.294 0.058 -5.044
## .awayhome1h_1y ~~
## .awayhome1h_3m 0.312 0.062 5.029
## .canue_Zdweldensity_pre18w ~~
## .rural -0.520 0.106 -4.911
## .num_siblings ~~
## .prenatal_smoke -0.129 0.027 -4.770
## .bw_sd ~~
## .num_siblings 0.096 0.020 4.687
## .no2.preg ~~
## .num_siblings -0.363 0.079 -4.594
## .condition_GestationalDiabetes_mother_prelabour ~~
## .mom_bmi_best 1.193 0.263 4.543
## .canue_Zdweldensity_pre18w ~~
## .num_siblings -0.179 0.040 -4.516
## .no2.y1 ~~
## .floor_carpt_3m -0.461 0.103 -4.492
## .no2.preg ~~
## .winter -0.586 0.131 -4.475
## .condition_GestationalDiabetes_mother_prelabour ~~
## .gest_days -2.177 0.499 -4.367
## .freq_score_basic ~~
## .mom_UPFContr 11.761 2.729 4.309
## .hometype_3m ~~
## .frntr_dnstyc_3 0.237 0.056 4.214
## .freq_score_basic ~~
## .mom_bmi_best 5.492 1.380 3.980
## .gest_days ~~
## .num_siblings -0.809 0.204 -3.969
## .mom_UPFContr ~~
## .postnatal_smok 1.781 0.450 3.955
## .cat_1y ~~
## .frntr_dnstyc_3 0.235 0.060 3.909
## .no2.y1 ~~
## .frntr_dnstyc_3 0.467 0.122 3.837
## .mom_bmi_best ~~
## .vaginal -0.685 0.179 -3.828
## .no2.preg ~~
## .frntr_dnstyc_3 0.515 0.135 3.805
## .num_siblings ~~
## .postnatal_smok -0.110 0.029 -3.777
## .no2.preg ~~
## .floor_carpt_3m -0.411 0.112 -3.670
## .no2.y1 ~~
## .Prgnncy.Tdn250 2.791 0.761 3.666
## .mom_bmi_best ~~
## .mom_UPFContr 5.109 1.394 3.664
## .hometype_3m ~~
## .Prgnncy.Tdn250 -2.025 0.567 -3.573
## .bf.1y ~~
## .freq_score_bsc -0.910 0.255 -3.564
## .num_siblings ~~
## .vaginal 0.101 0.029 3.507
## .freq_score_basic ~~
## .prenatal_smoke 1.346 0.386 3.488
## .masthma ~~
## .mom_bmi_best 0.619 0.182 3.403
## .no2.y1 ~~
## .num_siblings -0.234 0.070 -3.339
## .freq_score_basic ~~
## .no2.y1 -2.370 0.711 -3.335
## .dog_1y ~~
## .num_siblings -0.102 0.031 -3.328
## .cat_1y ~~
## .Prgnncy.Tdn250 2.757 0.838 3.290
## .maternaldistress_perceived_1y ~~
## .frntr_dnstyc_3 0.874 0.272 3.215
## .gest_days ~~
## .vaginal 1.097 0.344 3.188
## .freq_score_basic ~~
## .postnatal_smok 1.354 0.427 3.171
## .hometype_3m ~~
## .winter 0.171 0.055 3.131
## .canue_Zdweldensity_pre18w ~~
## .dog_1y -0.267 0.085 -3.130
## .freq_score_basic ~~
## .frntr_dnstyc_3 -1.214 0.390 -3.111
## .vaginal -1.145 0.375 -3.056
## .atbx.micro1y ~~
## .num_siblings 0.095 0.031 3.045
## .awayhome1h_3m ~~
## .cn_Zdwldnst_18 0.191 0.064 2.986
## .mom_bmi_best ~~
## .home_plant_3m -0.539 0.181 -2.977
## .condition_GestationalDiabetes_mother_prelabour ~~
## .mtrnldstrs__18 0.985 0.334 2.949
## .maternaldistress_perceived_pre18w ~~
## .prenatal_smoke 0.759 0.257 2.948
## .father_atopy_1y ~~
## .hometype_3m -0.171 0.058 -2.933
## .maternaldistress_perceived_pre18w ~~
## .mom_UPFContr 4.552 1.568 2.903
## .masthma ~~
## .mtrnldstrs__18 0.684 0.236 2.900
## .cat_1y ~~
## .postnatal_smok 0.185 0.064 2.897
## .prenatal_smoke 0.181 0.063 2.868
## .mom_bmi_best ~~
## .frntr_dnstyc_3 0.556 0.194 2.861
## .freq_score_basic ~~
## .mtrnldstrs__18 4.566 1.623 2.813
## .maternaldistress_perceived_pre18w ~~
## .mom_bmi_best 2.419 0.872 2.774
## .awayhome1h_3m ~~
## .mother_atpy_1y 0.167 0.060 2.756
## .bf.1y ~~
## .mom_UPFContr -0.501 0.182 -2.754
## .bw_sd ~~
## .hometype_3m -0.096 0.035 -2.738
## .fasthma ~~
## .floor_carpt_3m -0.155 0.057 -2.726
## .masthma ~~
## .mom_UPFContr 1.115 0.411 2.716
## .bf.1y ~~
## .mom_bmi_best -0.242 0.089 -2.707
## .atbx.m.birth ~~
## .mom_bmi_best 0.584 0.219 2.669
## .mom_UPFContr ~~
## .home_plant_3m -1.010 0.380 -2.660
## .canue_Zdweldensity_pre18w ~~
## .summer -0.138 0.052 -2.658
## .father_atopy_1y ~~
## .mtrnldstrs__18 0.669 0.253 2.647
## .dog_1y ~~
## .postnatal_smok 0.162 0.061 2.641
## .atbx.micro1y ~~
## .cndtn_GsttnD__ 0.225 0.085 2.639
## .maternaldistress_perceived_pre18w ~~
## .winter 0.604 0.230 2.628
## .bw_sd ~~
## .cn_Zdwldnst_18 -0.114 0.044 -2.590
## .atbx.micro1y ~~
## .freq_score_bsc 1.067 0.413 2.583
## .canue_Zdweldensity_pre18w ~~
## .mtrnldstrs__18 -0.721 0.280 -2.576
## .atbx.micro1y ~~
## .atbx.m.preg 0.170 0.066 2.573
## .mom_bmi_best 0.502 0.195 2.569
## .condition_GestationalDiabetes_mother_prelabour ~~
## .no2.y1 0.428 0.167 2.563
## .atbx.m.birth ~~
## .num_siblings -0.082 0.032 -2.539
## .awayhome1h_3m ~~
## .masthma 0.156 0.063 2.486
## .Pregnancy.Tdens250 ~~
## .floor_carpt_3m -1.518 0.614 -2.470
## .mother_atopy_1y ~~
## .Prgnncy.Tdn250 -1.584 0.644 -2.459
## .cat_1y ~~
## .floor_carpt_3m -0.132 0.054 -2.455
## .bf.1y ~~
## .atbx.micro1y -0.059 0.024 -2.436
## .prenatal_smoke -0.058 0.024 -2.410
## .atbx.m.preg ~~
## .mother_atpy_1y -0.137 0.058 -2.349
## .cat_1y ~~
## .cndtn_GsttnD__ -0.228 0.097 -2.347
## .freq_score_basic ~~
## .hometype_3m -0.929 0.399 -2.331
## .maternaldistress_perceived_pre18w ~~
## .no2.preg -1.302 0.562 -2.318
## .bw_sd ~~
## .fall -0.083 0.036 -2.308
## .bf.1y ~~
## .postnatal_smok -0.058 0.025 -2.295
## .maternaldistress_perceived_1y ~~
## .home_plant_3m -0.562 0.245 -2.295
## .freq_score_basic ~~
## .masthma 0.860 0.376 2.291
## .bf.1y ~~
## .home_plant_3m 0.047 0.021 2.288
## .atbx.m.birth ~~
## .gest_days -0.805 0.353 -2.282
## .hometype_3m ~~
## .prenatal_smoke 0.134 0.059 2.259
## .atbx.m.birth ~~
## .no2.preg -0.327 0.145 -2.255
## .freq_score_basic ~~
## .Prgnncy.Tdn250 -10.032 4.459 -2.250
## .awayhome1h_3m ~~
## .winter -0.148 0.066 -2.243
## .awayhome1h_1y ~~
## .vaginal -0.123 0.055 -2.242
## .atbx.m.preg ~~
## .freq_score_bsc 1.009 0.452 2.233
## .awayhome1h_1y ~~
## .prenatal_smoke 0.134 0.060 2.223
## .cat_1y ~~
## .mould_3m_mh 0.148 0.067 2.200
## .fasthma ~~
## .mom_bmi_best 0.471 0.216 2.185
## .bw_sd ~~
## .Prgnncy.Tdn250 1.015 0.465 2.184
## .awayhome1h_1y ~~
## .frntr_dnstyc_3 0.130 0.059 2.179
## .no2.preg 0.281 0.130 2.157
## .mother_atpy_1y 0.109 0.051 2.123
## .furniture_densityclutter_3m ~~
## .floor_carpt_3m -0.116 0.055 -2.119
## .father_atopy_1y ~~
## .masthma -0.117 0.056 -2.111
## .mom_bmi_best ~~
## .summer -0.414 0.197 -2.103
## .home_plant_3m ~~
## .floor_carpt_3m -0.100 0.048 -2.090
## .condition_GestationalDiabetes_mother_prelabour ~~
## .num_siblings -0.127 0.061 -2.085
## .bf.1y ~~
## .awayhome1h_1y -0.045 0.022 -2.081
## .rural -0.063 0.031 -2.030
## .cat_1y ~~
## .gest_days 0.784 0.393 1.996
## .dog_1y ~~
## .gest_days 0.764 0.384 1.989
## .postnatal_smoke ~~
## .rural 0.176 0.089 1.984
## .maternaldistress_perceived_1y ~~
## .mother_atpy_1y 0.496 0.251 1.979
## .atbx.micro1y ~~
## .gest_days -0.700 0.357 -1.963
## P(>|z|)
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.000
##
## 0.001
##
## 0.001
##
## 0.001
##
## 0.001
##
## 0.001
##
## 0.001
##
## 0.001
##
## 0.002
##
## 0.002
##
## 0.002
##
## 0.002
## 0.002
##
## 0.002
##
## 0.003
##
## 0.003
##
## 0.003
##
## 0.003
##
## 0.003
##
## 0.004
##
## 0.004
##
## 0.004
## 0.004
##
## 0.004
##
## 0.005
##
## 0.006
##
## 0.006
##
## 0.006
##
## 0.006
##
## 0.006
##
## 0.007
##
## 0.007
##
## 0.008
##
## 0.008
##
## 0.008
##
## 0.008
##
## 0.008
##
## 0.008
##
## 0.009
##
## 0.010
##
## 0.010
##
## 0.010
##
## 0.010
## 0.010
##
## 0.010
##
## 0.011
##
## 0.013
##
## 0.013
##
## 0.014
##
## 0.014
##
## 0.015
## 0.016
##
## 0.019
##
## 0.019
##
## 0.020
##
## 0.020
##
## 0.021
##
## 0.022
##
## 0.022
##
## 0.022
##
## 0.022
##
## 0.023
##
## 0.024
##
## 0.024
##
## 0.024
##
## 0.025
##
## 0.025
##
## 0.026
##
## 0.026
##
## 0.028
##
## 0.029
##
## 0.029
##
## 0.029
## 0.031
## 0.034
##
## 0.034
##
## 0.035
##
## 0.035
##
## 0.037
##
## 0.037
##
## 0.037
## 0.042
##
## 0.046
##
## 0.047
##
## 0.047
##
## 0.048
##
## 0.050
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .sp.1y.PCoA3 1.172 1.197 0.979 0.327
## .bf.1y 1.340 0.237 5.663 0.000
## .bw_sd 0.347 0.274 1.266 0.205
## .cn_Zdwldnst_18 0.181 0.474 0.383 0.702
## .freq_score_bsc 32.494 2.636 12.326 0.000
## .gest_days 277.831 2.689 103.323 0.000
## .mtrnldstrss__1 11.330 1.944 5.829 0.000
## .mtrnldstrs__18 11.809 1.745 6.767 0.000
## .mom_bmi_best 27.577 1.385 19.913 0.000
## .mom_UPFContr 48.816 2.821 17.302 0.000
## .no2.preg 0.747 0.888 0.841 0.400
## .no2.y1 3.807 0.871 4.369 0.000
## .num_siblings -0.042 0.208 -0.203 0.839
## .Prgnncy.Tdn250 20.873 3.977 5.249 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## atbx.micr1y|t1 1.503 0.444 3.388 0.001
## atbx.m.brth|t1 0.279 0.429 0.651 0.515
## atbx.m.preg|t1 0.506 0.523 0.968 0.333
## awayhm1h_1y|t1 2.446 0.406 6.020 0.000
## awayhm1h_3m|t1 1.935 0.500 3.873 0.000
## cat_1y|t1 1.053 0.417 2.526 0.012
## cndtn_GstD__|1 1.520 0.829 1.834 0.067
## dog_1y|t1 0.057 0.430 0.133 0.894
## fasthma|t1 0.248 0.456 0.544 0.586
## fthr_tpy_1y|t1 -0.651 0.410 -1.590 0.112
## hometype_3m|t1 0.881 0.395 2.232 0.026
## masthma|t1 1.048 0.382 2.746 0.006
## mthr_tpy_1y|t1 -0.183 0.371 -0.494 0.621
## postntl_smk|t1 1.036 0.430 2.411 0.016
## prenatl_smk|t1 0.209 0.392 0.534 0.594
## rural|t1 2.693 0.595 4.529 0.000
## frntr_dnst_3|1 0.348 0.421 0.827 0.408
## hom_plnt_3m|t1 -0.408 0.363 -1.126 0.260
## flr_crpt_3m|t1 0.268 0.373 0.717 0.474
## mould_3m_mh|t1 0.490 0.437 1.121 0.262
## vaginal|t1 -0.303 0.402 -0.753 0.451
## fall|t1 0.501 0.381 1.313 0.189
## summer|t1 1.164 0.387 3.008 0.003
## winter|t1 -0.278 0.402 -0.691 0.489
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .sp.1y.PCoA3 0.839 0.065 12.949 0.000
## .bf.1y 0.217 0.054 3.998 0.000
## .atbx.micro1y 1.000
## .atbx.m.birth 1.000
## .atbx.m.preg 1.000
## .awayhome1h_1y 1.000
## .awayhome1h_3m 1.000
## .bw_sd 0.867 0.034 25.564 0.000
## .cn_Zdwldnst_18 2.275 0.063 36.389 0.000
## .cat_1y 1.000
## .cndtn_GsttnD__ 1.000
## .dog_1y 1.000
## .fasthma 1.000
## .father_atpy_1y 1.000
## .freq_score_bsc 93.066 3.868 24.058 0.000
## .gest_days 84.378 3.137 26.902 0.000
## .hometype_3m 1.000
## .masthma 1.000
## .mtrnldstrss__1 40.847 1.847 22.121 0.000
## .mtrnldstrs__18 35.320 1.546 22.850 0.000
## .mom_bmi_best 24.007 0.813 29.536 0.000
## .mom_UPFContr 101.605 3.974 25.568 0.000
## .mother_atpy_1y 1.000
## .no2.preg 9.923 0.255 38.860 0.000
## .no2.y1 7.820 0.201 38.944 0.000
## .num_siblings 0.580 0.024 23.917 0.000
## .postnatal_smok 1.000
## .Prgnncy.Tdn250 225.598 7.858 28.710 0.000
## .prenatal_smoke 1.000
## .rural 1.000
## .frntr_dnstyc_3 1.000
## .home_plant_3m 1.000
## .floor_carpt_3m 1.000
## .mould_3m_mh 1.000
## .vaginal 1.000
## .fall 1.000
## .summer 1.000
## .winter 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ind_.1.PCA3_.1 -0.074 0.036 -2.073 0.038
## ind_.1.PCA3_.1 -0.004 0.007 -0.624 0.533
## ind_.1.PCA3_.. 0.001 0.008 0.185 0.853
## ind_.1.PCA3_.. -0.007 0.009 -0.816 0.415
## in_.1.PCA3_1_1 0.000 0.024 0.009 0.993
## in_.1.PCA3_1_3 -0.017 0.026 -0.650 0.516
## ind_s.1.PCA3__ -0.005 0.006 -0.928 0.354
## i_.1.PCA3__Z_1 0.006 0.007 0.849 0.396
## ind_.1.PCA3__1 -0.000 0.002 -0.094 0.925
## i_.1.PCA3__GD_ -0.023 0.040 -0.578 0.564
## ind_.1.PCA3__1 -0.001 0.005 -0.233 0.816
## ind_sp.1.PCA3_ -0.001 0.004 -0.343 0.731
## in_.1.PCA3___1 -0.005 0.011 -0.461 0.645
## ind_.1.PCA3___ -0.001 0.003 -0.253 0.800
## ind_s.1.PCA3__ -0.002 0.009 -0.262 0.793
## ind_.1.PCA3__3 -0.013 0.065 -0.204 0.839
## ind_sp.1.PCA3_ -0.000 0.002 -0.137 0.891
## in_.1.PCA3___1 -0.053 0.086 -0.608 0.543
## i_.1.PCA3___18 0.042 0.084 0.503 0.615
## ind_.1.PCA3___ 0.006 0.019 0.334 0.738
## i_.1.PCA3__UPF 0.003 0.010 0.272 0.786
## in_.1.PCA3___1 -0.001 0.004 -0.337 0.736
## ind_.1.PCA3_2. -0.035 0.029 -1.213 0.225
## in_.1.PCA3_2.1 -0.009 0.023 -0.383 0.702
## ind_s.1.PCA3__ 0.001 0.009 0.103 0.918
## ind_s.1.PCA3__ 0.027 0.109 0.250 0.803
## i_.1.PCA3_P.T2 -0.002 0.011 -0.179 0.858
## ind_s.1.PCA3__ -0.033 0.095 -0.343 0.731
## ind_sp.1.PCA3_ 0.024 0.045 0.530 0.596
## in_.1.PCA3___3 -0.007 0.032 -0.224 0.822
## in_.1.PCA3___3 0.010 0.011 0.864 0.388
## in_.1.PCA3___3 0.007 0.012 0.573 0.567
## in_.1.PCA3__3_ 0.003 0.011 0.281 0.779
## ind_sp.1.PCA3_ -0.000 0.001 -0.020 0.984
## ind_sp.1.PCA3_ -0.001 0.003 -0.420 0.674
## ind_sp.1.PCA3_ -0.004 0.005 -0.816 0.415
## ind_sp.1.PCA3_ 0.006 0.007 0.797 0.425Generate dispersion boxplot (Figure 3A)
pdf("Figures/Dispersion figure.pdf",width=3,height=10)
data.withmissing.bf <- merge(data.withmissing,subset(data.div, Visit=="3 month"),by.x="subjectnumber", by.y="SubjectNumber")
data.withmissing.bf$bf <- ifelse(data.withmissing.bf$exact_age*12<data.withmissing.bf$bf_duration_imp,1, 0)
nobf.id <- unique(data.withmissing.bf$subjectnumber[data.withmissing.bf$bf==0 & !is.na(data.withmissing.bf$bf) & data.withmissing.bf$bf_duration_imp<3])
data.add.bf <- (varespec.bray.use$`3 month_Species`[[2]])
data.add.bf$bf <- ifelse(data.add.bf$exact_age*12<data.add.bf$bf_duration_imp,1,
ifelse(data.add.bf$subjectnumber%in%as.character(nobf.id),0,NA))
bd <- vegan::betadisper(varespec.bray.use$`3 month_Species`[[1]], data.add.bf$bf)
boxplot(bd)
df <- data.frame(Distance_to_centroid=bd$distances,Group=bd$group)
groups <- bd$group
p<- ggplot(data=df,aes(x=Group,y=Distance_to_centroid,fill=groups))
p <-p + geom_boxplot()
p <-p + theme(axis.text.x=element_text(angle = -90, hjust = 0,vjust=0.5)) + stat_compare_means() +theme_bw() +
scale_fill_manual(values = c("#f7d0c6","#e76f51"))
p
dev.off()## quartz_off_screen
## 2p
pdf("Figures/Dispersion figure2.pdf",width=3,height=10)
data.add.bf <- (varespec.bray.use$`1 year_Species`[[2]])
data.add.bf$bf <- ifelse(data.add.bf$exact_age*12<data.add.bf$bf_duration_imp,1,
ifelse(data.add.bf$subjectnumber%in%as.character(nobf.id),0,NA))
bd <- vegan::betadisper(varespec.bray.use$`1 year_Species`[[1]], data.add.bf$bf)
boxplot(bd)
df <- data.frame(Distance_to_centroid=bd$distances,Group=bd$group)
groups <- bd$group
p<- ggplot(data=df,aes(x=Group,y=Distance_to_centroid,fill=groups))
p <-p + geom_boxplot()
p <-p + theme(axis.text.x=element_text(angle = -90, hjust = 0,vjust=0.5)) + stat_compare_means() +theme_bw()+
scale_fill_manual(values = c("#f7d0c6","#e76f51"))
p
dev.off()## quartz_off_screen
## 2p
Generate bootstrap PERMANOVA results (Figure 3B)
# rm(list=ls())
# library(phyloseq)
# library(psych)
# library(corrplot)
# library("psych")
# library(ggplot2)
# library(car)
# library(magrittr)
# library(ggcorrplot)
# library(missForest)
# library(xlsx)
# library(WGCNA)
# library(ggpubr)
# library(lavaan)
# library(limma)
# library(metaMint)
# library(lubridate)
# source("/Users/darlene.dai/Desktop/Darlene Dai/Common function/UVA&MVA function.R")
# source("/Users/darlene.dai/Desktop/Darlene Dai/Common function/Demographic Function.R")
#
# work.dir <- "/Users/darlene.dai/Desktop/SES/Results/manuscript/Code/"
# setwd(work.dir)
# load("Results/P1 Processed Metagenomic data.RData")
# load("Results/P2 Univariate Analysis of SES.Rdata")
#
# UVA.res.all <- subset(UVA.res.outcome, !Variables%in%c("sitetoronto","sitevancouver","sitewinnipeg","Season2.Summer","Season3.Fall","Season4.Winter"))
# cov.vars <- c("MR1_latent.raw","male",
# "atbx.microbiome.new", "atbx.m.birth", "atbx.m.preg", "awayhome1h_1y" ,
# "awayhome1h_3m", "bf","bw_sd", "canue_Zdweldensity_pre18w" ,
# "cat_1y", "condition_GestationalDiabetes_mother_prelabour",
# "dog_1y", "fasthma","father_atopy_1y",
# "freq_score_basic", "gest_days", "hometype_3m","masthma","maternaldistress_perceived_1y",
# "maternaldistress_perceived_pre18w", "mom_bmi_best", "mom_UPFContr", "mother_atopy_1y",
# "no2.preg", "no2.y1", "num_siblings", "postnatal_smoke",
# "Pregnancy.Tdens250", "prenatal_smoke", "rural", "furniture_densityclutter_3m",
# "home_plant_3m", "floor_carpet_3m", "mould_3m_mh", "vaginal", "fall","summer", "winter")
#
# data.withmissing.bf <- merge(data.withmissing,subset(data.div, Visit=="3 month"),by.x="subjectnumber", by.y="SubjectNumber")
# data.withmissing.bf$bf <- ifelse(data.withmissing.bf$exact_age*12<data.withmissing.bf$bf_duration_imp,1, 0)
# nobf.id <- unique(data.withmissing.bf$subjectnumber[data.withmissing.bf$bf==0 & !is.na(data.withmissing.bf$bf) & data.withmissing.bf$bf_duration_imp<3])
# nobf.id2 <- unique(data.withmissing.bf$subjectnumber[data.withmissing.bf$bf==0 & !is.na(data.withmissing.bf$bf)])
#
# ############################################################################################################
# library(vegan)
# technical.vars <- c("ProcessingPeriod" ,"exact_age")
#
# PERMANOVA.UVA <- NULL
# visit.all <- c("1 year","3 month")
# vars.use <- cov.vars
#
# cohorts <- c( "subBF","NoBF")
# bootstrap.is <- 100
# set.seed(2423987)
# seed.used <- sample(10000:1000000, bootstrap.is)
#
# for(bootstrap.i in 1:bootstrap.is){
# for(cohort in cohorts){
# for(data.type in c("Species")){
# if(data.type=="Species"){
# OTU.use <- RBNME_species_MCLR
# }
# if(data.type=="META"){
# OTU.use <- RBMETA
#
# }
# for(visit in visit.all){
# if(visit%in%c("3 month", "1 year")){
# s.use <- as.character(data.div$SampleID[data.div$Visit==visit])
# s.use.subjectid <- as.character(data.div$SubjectNumber[data.div$Visit==visit])
# }
# for(missing in c("raw")){
# if(missing=="raw"){data.cli.p <- data.withmissing[data.withmissing$subjectnumber%in%s.use.subjectid,]}
# if(missing=="impute"){data.cli.p <- data.imputation[data.imputation$subjectnumber%in%s.use.subjectid,]}
# data.cli.p <- merge(data.cli.p, data.div[s.use,c("SubjectNumber","SampleID","Visit","exact_age","ProcessingPeriod")],
# by.x="subjectnumber", by.y="SubjectNumber")
# data.cli.p$atbx.microbiome.new <- ifelse(data.cli.p$Visit=="3 month", as.numeric(as.character(data.cli.p$atbx.micro3m)),
# as.numeric(as.character(data.cli.p$atbx.micro1y)))
# data.cli.p$bf <- ifelse(data.cli.p$exact_age*12<data.cli.p$bf_duration_imp,1, 0)
#
# if(cohort=="subBF"){
# data.cli.p <- subset(data.cli.p, bf==1)
# vars.use <- vars.use[!vars.use%in%c("bf")]
# }
# if(cohort=="NoBF"){
# data.cli.p <- subset(data.cli.p, subjectnumber%in%as.character(nobf.id))
# vars.use <- vars.use[!vars.use%in%c("bf")]
# }
# if(visit%in%c("3 month", "1 year")){
# rownames(data.cli.p) <- data.cli.p$subjectnumber
# }
# s.overlap <- data.cli.p$subjectnumber
# s.overlap <- as.character(s.overlap)
# otu.table.use <- data.frame(otu_table(OTU.use))
# otu.table.sub <- otu.table.use[s.use,]
# rownames(otu.table.sub) <- s.use.subjectid
#
# for(var.use in vars.use){
#
# var.model.use <- c("ProcessingPeriod" ,"exact_age", var.use)
# full.model <- paste("otu.table.model ~ ", paste(var.model.use, collapse = "+"))
# data.cli.p.nomissing <- na.omit(data.cli.p[,c(var.model.use,"site")])
#
# s.nomissing <- as.character(rownames(data.cli.p.nomissing))
# set.seed(seed.used[bootstrap.i])
# s.overlap.bootstrap <- (sample(s.nomissing,188,replace = T))
#
# data.cli.model <- data.cli.p[s.overlap.bootstrap,]
# otu.table.model <- otu.table.sub[s.overlap.bootstrap,]
# rownames(data.cli.model)
# rownames(otu.table.model)
# set.seed(343834)
# margin.permanova <- adonis2(as.formula(full.model),
# data = data.cli.model,
# permutations = 999,
# method="euclidean",
# by="margin",
# strata =data.cli.model$site)
#
# res.margin <- data.frame(Data=data.type,
# Imputation=missing,
# Visit=visit,
# Cohort=cohort,
# Var=c(var.model.use,"Residual","Total"),
# N=length(s.nomissing),
# Tes=var.use,
# R2.margin=margin.permanova$R2,
# Fvalue.margin=margin.permanova$F,
# p.margin=margin.permanova$`Pr(>F)`,
# bootstrap=bootstrap.i)
#
# res <- res.margin
# PERMANOVA.UVA <- rbind(PERMANOVA.UVA, res)
# save(PERMANOVA.UVA, file="Results/P3 UVA PERMANOVA subcohort Bootstrap188.RData")
# }
# }
# }
# }
# }
# }
load("Results/P3 UVA PERMANOVA subcohort Bootstrap188.RData")
data.map.use <- data.map
PERMANOVA.all <- subset(PERMANOVA.UVA, Var%in%cov.vars[!cov.vars%in%"EBF_6m"])
PERMANOVA.all[PERMANOVA.all$Var%in%c("no2.y1","maternaldistress_perceived_1y","awayhome1h_1y") & PERMANOVA.all$Visit=="3 month","R2.margin"] <- 0
PERMANOVA.all[PERMANOVA.all$Var%in%c("no2.y1","maternaldistress_perceived_1y","awayhome1h_1y") & PERMANOVA.all$Visit=="3 month","p.margin"] <- NA
PERMANOVA.all[PERMANOVA.all$Var%in%c("no2.y1","maternaldistress_perceived_1y","awayhome1h_1y") & PERMANOVA.all$Visit=="3 month","Fvalue.margin"] <- NA
PERMANOVA.all.plot <- merge(PERMANOVA.all,data.map.use ,by.x="Var", by.y="Vars",all.x=T)
PERMANOVA.all.plot$Visit <- factor(PERMANOVA.all.plot$Visit, levels=c("3 month", "1 year"), ordered=T)
PERMANOVA.all.plot$U <- ifelse(PERMANOVA.all.plot$p.margin<0.05, "+","")
PERMANOVA.all.plot$Data <- factor(PERMANOVA.all.plot$Data, levels=c( "Species","META"), ordered=T)
PERMANOVA.all.plot <- subset(PERMANOVA.all.plot, bootstrap%in%c(1:100))
dd <- unique(subset(PERMANOVA.all.plot))
rank.dd <- ddply(dd, .(Label,Category,Visit,Cohort), summarise,
mean.log.p2=mean(-log10(p.margin )),
mean.r2=mean(R2.margin),
se=1.96 * sd(R2.margin) / sqrt(length(R2.margin)),
sig.freq=length(U[U=="+"])/length(U))
rank.dd <- rank.dd[order(rank.dd$mean.r2,decreasing = F),]
rank.dd$Label <- factor(rank.dd$Label, levels=unique(dd$Label), ordered=T)
rank.dd$Significance.prev <- ifelse(rank.dd$sig.freq>=0.8,1,0)
rank.dd$Significance.mean<-ifelse(rank.dd$mean.log.p2>log10(0.05)*-1, 1, 0)
rank.dd$mean.r2 <- rank.dd$mean.r2*100
rank.dd$se<- rank.dd$se * 100
rank.dd$mean.r2.neg<-ifelse(rank.dd$Cohort=="NoBF", rank.dd$mean.r2 * -1, rank.dd$mean.r2 )
rank.dd$se.neg<-ifelse(rank.dd$Cohort=="NoBF", rank.dd$se * -1, rank.dd$se)
rank.dd$Cohort<-factor(rank.dd$Cohort, levels = c("SubBF","NoBF"))
rank.dd$Category <- factor(rank.dd$Category, levels=c("SES","Pregnancy","Birth",
"Postnatal",
"Home Environment",
"Neighbourhood Environment",
"Parental Diet/Health"), ordered=T)
rank.dd$Label <- factor(rank.dd$Label, levels=rev(layer_scales(permanova.plot, 1, 1)$y$range$range),ordered=T)
plot.subbootstrap <- ggplot(subset(rank.dd,!is.na(Category) &Label!="SES Index"), aes(x=Label, y=mean.r2.neg, fill=Category, color=Category, alpha=Significance.prev)) +
geom_bar(position=position_dodge(), stat="identity") +
geom_errorbar(aes(ymin= mean.r2.neg - se.neg,
ymax= mean.r2.neg + se.neg),
width=.6,
position=position_dodge(.9))+
facet_grid(Visit~Category,space="free", scales = "free_x")+
scale_fill_manual(values = c("#E9C46A","#F4A261","#E76F51","#2A9D8F","#287271","#9A4770"))+
scale_color_manual(values = c("#E9C46A","#F4A261","#E76F51","#2A9D8F","#287271","#9A4770"))+
theme_bw() +
geom_hline(yintercept = 0)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
ylab("Mean of Beta-diversity variance explained (R2) %")+
coord_cartesian(ylim = c(-2.3, 2.3))
plot.subbootstrap
pdf("Figures/P3 bootstrap PERMANOVA R2_Dec12.pdf", height=10, width=16)
plot.subbootstrap
dev.off()## quartz_off_screen
## 2Run Maaslin2 analysis (Figure 4, 5)
Identify the species associated with SES
rm(list=ls())
library(phyloseq)
library(psych)
library(corrplot)
library("psych")
library(ggplot2)
library(car)
library(magrittr)
library(ggcorrplot)
library(missForest)
library(xlsx)
library(WGCNA)
library(ggpubr)
library(lavaan)
library(limma)
library(metaMint)
library(lubridate)
library(Maaslin2)
library(tidyverse)
library(phyloseq)
library(copiome)
library(ggpubr)
library(ggforce)
library(patchwork)
library(gtsummary)
library(Maaslin2)
library(scales)
library("ggsci")
library(dplyr)
library(cowplot)
library(plyr)
library(reshape2)
library(colorspace)
work.dir <- "/Users/darlene.dai/Desktop/SES/Results/manuscript/Code/"
# work.dir <- "~/Documents/SES_Manuscript/"
setwd(work.dir)
#####################################################################
load("Results/P1 Processed Metagenomic data.RData")
# Maaslin2 analysis (no need to run again)
#Remove the samples not prevalent enough
remove_rare <- function( table , cutoff_pro ) {
row2keep <- c()
cutoff <- ceiling( cutoff_pro * ncol(table) )
for ( i in 1:nrow(table) ) {
row_nonzero <- length( which( table[ i , ] > 0 ) )
if ( row_nonzero > cutoff ) {
row2keep <- c( row2keep , i)
}
}
return( table [ row2keep , , drop=F ])
}
library(vegan)
SES.maaslin <- NULL
SES.maaslin <- unique(SES.maaslin)
SES.vars <- c("MR1_latent.raw")
outcome.vars <- c("atopy5y", "asthma5y_ep", "overweight", "Tscore_abnormal")
cov.vars <- c("atbx.microbiome.new", "bf","EBF_6m","male",
"atbx.m.birth", "atbx.m.preg", "awayhome1h_1y" ,
"awayhome1h_3m","bw_sd", "canue_Zdweldensity_pre18w" ,
"cat_1y", "condition_GestationalDiabetes_mother_prelabour",
"dog_1y", "fasthma","father_atopy_1y",
"freq_score_basic", "gest_days", "hometype_3m","masthma","maternaldistress_perceived_1y",
"maternaldistress_perceived_pre18w", "mom_bmi_best", "mom_UPFContr", "mother_atopy_1y",
"no2.preg", "no2.y1", "num_siblings", "postnatal_smoke",
"Pregnancy.Tdens250", "prenatal_smoke", "rural", "furniture_densityclutter_3m",
"home_plant_3m", "floor_carpet_3m", "mould_3m_mh", "vaginal", "fall","summer", "winter",
"bfhistory_m","bfhistory_m_time",
"bfhistory_f","bfhistory_f_time",
"PBscore",
"WSscore",
"BAscore","eaPBscore",
"eaWSscore" ,
"eaBAscore",
"pb_sumscore",
"ws_sumscore",
"ba_sumscore" ,
"eapb_sumscore",
"eaws_sumscore",
"eaba_sumscore","B2")
used.vars <- c(SES.vars, outcome.vars, cov.vars)
data.withmissing.bf <- merge(data.withmissing,subset(data.div, Visit=="3 month"),by.x="subjectnumber", by.y="SubjectNumber")
data.withmissing.bf$bf <- ifelse(data.withmissing.bf$exact_age*12<data.withmissing.bf$bf_duration_imp,1, 0)
nobf.id <- unique(data.withmissing.bf$subjectnumber[data.withmissing.bf$bf==0 & !is.na(data.withmissing.bf$bf) &
data.withmissing.bf$bf_duration_imp<3])
prev.cts <- c(0.1)
adj.vars.basics <- list(c("ProcessingPeriod", "exact_age"))
SES.maaslin <- NULL
# for(data.type in c("DIV","Species","META")){
# if(data.type%in%c("DIV")){
# norm.methods <- c("raw")
# if(data.type=="DIV"){
# visit.all <- c("3 month", "1 year","longitudinal")#
# }else{
# visit.all <- c("1 year")
# }
# }else{
# norm.methods <- "MCLR"
# visit.all <- c("longitudinal")
# }
# for(norm.method in norm.methods){
#
# if(norm.method=="raw" & data.type=="DIV"){
# RBNME.use <- DIV
# norm.use <- "none"
# trans.use <- "none"
# }
#
# if(norm.method=="MCLR"){
# if(data.type=="Species"){
# RBNME.use <- RBNME_species_MCLR
# }
# if(data.type=="KEGGNAMED"){
# RBNME.use <- RBKEGGNAMED_MCLR
# }
# if(data.type=="META"){
# RBNME.use <- RBMETA_MCLR
# }
#
# norm.use <- "none"
# trans.use <- "none"
# }
# if(norm.method=="CLR"){
# if(data.type=="Species"){
# RBNME.use <- RBNME_species_CLR
# }
# if(data.type=="KEGGNAMED"){
# RBNME.use <- RBKEGGNAMED_CLR
# }
# if(data.type=="META"){
# RBNME.use <- RBMETA_CLR
# }
# norm.use <- "none"
# trans.use <- "none"
# }
# if(norm.method=="TSSLOG"){
# if(data.type=="Species"){
# RBNME.use <- RBNME_species
# }
# if(data.type=="KEGGNAMED"){
# RBNME.use <- RBKEGGNAMED
# }
# if(data.type=="META"){
# RBNME.use <- RBMETA
# }
# norm.use <- "none"
# trans.use <- "LOG"
# }
# for(adj.vars.basic in adj.vars.basics){
# for(visit in visit.all){
# OTU.use <- RBNME.use
# for(prev.ct in prev.cts){
# min.use <- -Inf
# prev.use <- -Inf
# if(prev.ct==-Inf){
# used.sp <- colnames(otu_table(RBNME.use))
#
# }else{
# if(visit!="longitudinal"){
# if(data.type=="Species"){
# RBNME.use.pvt <- RBNME_species
# }
# if(data.type=="KEGGNAMED"){
# RBNME.use.pvt <- RBKEGGNAMED
# }
# if(data.type=="META"){
# RBNME.use.pvt <- RBMETA
# }
# if(data.type=="DIV"){
# RBNME.use.pvt <- DIV
# }
#
# otu.use.sgv <- subset_samples(RBNME.use.pvt, Visit==visit)
# otu.use.sgv <- remove_rare(table=t(otu_table(otu.use.sgv)), cutoff_pro=0)
# used.sp<-rownames(otu.use.sgv)
# }
# if(visit=="longitudinal"){
# if(data.type=="Species"){
# RBNME.use.pvt <- RBNME_species
# }
# if(data.type=="KEGGNAMED"){
# RBNME.use.pvt <- RBKEGGNAMED
# }
# if(data.type=="META"){
# RBNME.use.pvt <- RBMETA
# }
# if(data.type=="DIV"){
# RBNME.use.pvt <- DIV
# }
# otu.use.sgv <- RBNME.use.pvt
# otu.use.sgv <- remove_rare(table=t(otu_table(otu.use.sgv)), cutoff_pro=0)
# #remove metaphlan.table.SGB.s.3m with prevalence less than 10%
# used.sp<-rownames(otu.use.sgv)
# }
# }
#
# if(visit=="longitudinal"){
# s.use.subjectid <- names(which(table(data.div$SubjectNumber)>0))
# s.use <- as.character(data.div$SampleID[data.div$SubjectNumber%in%s.use.subjectid])
# }
# if(visit%in%c("3 month", "1 year")){
# s.use <- as.character(data.div$SampleID[data.div$Visit==visit])
# s.use.subjectid <- as.character(data.div$SubjectNumber[data.div$Visit==visit])
# }
# for(missing in c("raw")){
# if(missing=="raw"){data.cli.p <- data.withmissing[data.withmissing$subjectnumber%in%s.use.subjectid,]}
# data.cli.p <- merge(data.cli.p,
# data.div[s.use,c("SubjectNumber","Visit","SampleID","exact_age","ProcessingPeriod")],
# by.x="subjectnumber", by.y="SubjectNumber")
#
# data.cli.p <- subset(data.cli.p, exact_age<1.5)
# data.cli.p$atbx.microbiome <- ifelse(data.cli.p$atbx.1y.time<data.cli.p$exact_age*365,1,0)
# data.cli.p$atbx.microbiome.new <- ifelse(data.cli.p$Visit=="3 month", as.numeric(as.character(data.cli.p$atbx.micro3m)),as.numeric(as.character(data.cli.p$atbx.micro1y)))
#
# data.cli.p$bf <- ifelse(data.cli.p$exact_age*12<data.cli.p$bf_duration_imp,1, 0)
#
# if(visit%in%c("3 month", "1 year")){
# rownames(data.cli.p) <- data.cli.p$subjectnumber
# }
# if(visit=="longitudinal"){
# rownames(data.cli.p) <- data.cli.p$SampleID
# }
# for(bf.sub in c("NoBF","AnyBF","All")){#,
#
# if(bf.sub=="All"){s.overlap <- rownames(data.cli.p)}
# if(bf.sub=="AnyBF"){
# s.overlap <- rownames(data.cli.p)[data.cli.p$bf==1 & !is.na(data.cli.p$bf)]
# }
# if(bf.sub=="NoBF"){
# s.overlap <- rownames(data.cli.p)[data.cli.p$subjectnumber%in%nobf.id]
# }
#
# s.overlap <- as.character(s.overlap)
# data.cli.p.use <- data.cli.p[s.overlap,]
# otu.table.use <- as.matrix(otu_table(OTU.use))
# otu.table.sub <- otu.table.use[as.character(s.use),]
#
# if(visit%in%c("3 month", "1 year")){
# rownames(otu.table.sub) <- s.use.subjectid
# }
# if(visit=="longitudinal"){
# rownames(otu.table.sub) <- s.use
# }
# otu.table.sub <- otu.table.sub[s.overlap,]
#
# for(adj.race in c("none")){#"genetic",
# if(adj.race=="none"){
# adj.vars <- adj.vars.basic
# used.vars.use <- used.vars[!used.vars%in%c(paste0("ethnicity_pc",1:3))]
# }
# if(bf.sub%in%c("AnyBF","NoBF")){
# used.vars.bf <- used.vars.use[!used.vars.use%in%c("bf" ,"EBF_6m")]
# }else{
# used.vars.bf <- used.vars.use
# }
#
# adj.vars.model <- adj.vars
# used.vars.model <- used.vars.bf
#
# for(check.var in c( "B2")){#c(used.vars.model)
# all.vars <- unique(c(check.var, adj.vars.model))
# data.cli.p.nomissing <- na.omit(data.cli.p.use[,all.vars])
#
# s.nomissing <- as.character(rownames(data.cli.p.nomissing))
#
# data.cli.model <- data.cli.p.use[s.nomissing,]
# otu.table.model <- otu.table.sub[s.nomissing,]
# data.cli.model[,check.var] <- scale(as.numeric(as.character(data.cli.model[,check.var])))
# data.cli.model.use <- data.cli.model
# need.change.sp <- used.sp[!used.sp%in%colnames(otu.table.model)]
# used.sp[used.sp%in%need.change.sp] <- paste0("X",used.sp[used.sp%in%need.change.sp])
# otu.table.model.use <- otu.table.model[rownames(data.cli.model.use),used.sp]
# otu.table.model.use <- na.omit(otu.table.model.use)
# data.cli.model.use <- data.cli.model.use[rownames(otu.table.model.use),]
# if(visit%in%c("3 month", "1 year")){
# masslin.res <- Maaslin2(
# input_data = otu.table.model.use,
# input_metadata = data.cli.model.use,
# output = "Metagenomic",
# normalization = norm.use,
# transform = trans.use,
# min_abundance = min.use,
# min_prevalence = prev.use,
# plot_heatmap = FALSE,
# plot_scatter = FALSE,
# fixed_effects = c(all.vars),
# random_effects = "site")
#
# res <- masslin.res$results
# res <- subset(res)
# res$N <- nrow(otu.table.model)
# res$Model <- "SingleVisit"
# }
#
# if(visit%in%c("longitudinal")){
#
# data.cli.model.use[,check.var] <- as.numeric(as.character(data.cli.model.use[,check.var]))
# data.cli.model.use$time_interaction <- data.cli.model.use$exact_age*data.cli.model.use[,check.var]
#
# masslin.res.long <- Maaslin2(
# input_data = otu.table.model.use,
# input_metadata = data.cli.model.use,
# output = "Metagenomic",
# normalization = norm.use,
# transform = trans.use,
# min_abundance = min.use,
# min_prevalence = prev.use,
# plot_heatmap = FALSE,
# plot_scatter = FALSE,
# fixed_effects = all.vars,
# random_effects = c("subjectnumber","site"))
# masslin.res.long.int <- Maaslin2(
# input_data = otu.table.model.use,
# input_metadata = data.cli.model.use,
# output = "Metagenomic",
# normalization = norm.use,
# transform = trans.use,
# min_abundance = min.use,
# min_prevalence = prev.use,
# plot_heatmap = FALSE,
# plot_scatter = FALSE,
# fixed_effects = c(all.vars,"time_interaction"),
# random_effects = c("subjectnumber","site"))
#
# res1 <- masslin.res.long$results
# res2 <- masslin.res.long.int$results
# res1$Model <- "Nointeract"
# res2$Model <- "Interact"
#
# res <- rbind(res1, res2)
# res$N <- nrow(otu.table.model)
# }
#
# res.all <- res
# res.all$check.var <- check.var
# res.all$Data <- data.type
# res.all$Visit <- visit
# res.all$Race.Adj <- adj.race
# res.all$BFsub <- bf.sub
# res.all$adjbasic <- paste(adj.vars.basic, collapse = " + ")
# res.all$Normalization <- norm.method
# res.all$Prevalence_cutoff <- prev.ct
# SES.maaslin <- rbind(SES.maaslin, res.all)
# save(SES.maaslin, file="Results/P5 Maaslin2 Results.RData")
#
# }
# }
# }
# }
# }
# }
# }
# }
# }
load("Results/P5 Maaslin2 Results.RData")Summarize the results from MAaslin2 analysis
SES.maaslin.combine <- unique(SES.maaslin)
######################################################################
#Combine the models with and without interaction. If slope not significant (p-value > 0.05), use the models without interaction.
qval.ct <- 0.1
exclude.val <- c("toronto","vancouver","winnipeg","square_age","exact_age","ProcessingPeriod","1")
check.var.use <- unique(SES.maaslin.combine$check.var)
SES.masslin.all <- NULL
adj.vars.basics <- list(c("ProcessingPeriod", "exact_age","site"))
for(BFsub.use in c("All","AnyBF","NoBF")){
for(adj.vars.basic.use in unique(SES.maaslin.combine$adjbasic)){
for(norm.use in c("MCLR")){
SES.masslin.new <- subset(SES.maaslin.combine, BFsub==BFsub.use & Race.Adj=="none" &
Normalization==norm.use & adjbasic==adj.vars.basic.use &
Visit=="longitudinal" & Data=="Species" & Prevalence_cutoff==0.1)
for(check.var.i in check.var.use){
ses.sp.int <- subset(SES.masslin.new, check.var%in%check.var.i & value%in%c("time_interaction") & !value%in%exclude.val & Model=="Interact" )
ses.sp.main <- subset(SES.masslin.new, check.var%in%check.var.i & !value%in%exclude.val & Model=="Nointeract" )
ff.no.int <- subset(ses.sp.int, pval>=0.05)
ff.sig.int <- subset(ses.sp.int, pval<0.05)
ses.sp.int.all <- subset(SES.masslin.new, check.var%in%check.var.i & value%in%c("time_interaction",check.var.i,"exact_age") &
Model=="Interact" )
ses.sp.main.all <- subset(SES.masslin.new, check.var%in%check.var.i & value%in%c(check.var.i,"exact_age") & Model=="Nointeract")
ses.new.i <- rbind(ses.sp.int.all,
ses.sp.main.all)
SES.masslin.all <- rbind(SES.masslin.all, ses.new.i)
}
}
}
}
SES.masslin.all$feature_update <- strsplit2(SES.masslin.all$feature,"__")[,1]
dup.name <- strsplit2(unique(SES.masslin.all$feature),"__")[,1][duplicated(strsplit2(unique(SES.masslin.all$feature),"__")[,1])]
SES.masslin.all$feature_update[SES.masslin.all$feature_update %in%dup.name] <- SES.masslin.all$feature[SES.masslin.all$feature_update %in%dup.name]
SES.masslin.all$feature_update <- gsub(" "," ",gsub("_"," ",SES.masslin.all$feature_update))
SES.masslin.all <- unique(SES.masslin.all)
SES.masslin.all.use <- subset(SES.masslin.all, Normalization=="MCLR" & BFsub=="All" &
adjbasic=="ProcessingPeriod + exact_age" )
######################################################################
######################################################################
#Longitudinal analysis results for SES
check.var.use <- c("MR1_latent.raw")
#Species associated with SES
ses.sp.int <- subset(SES.masslin.all.use,
qval<qval.ct & check.var%in%check.var.use & value%in%c("time_interaction"))
ses.sp.main <- subset(SES.masslin.all.use, Model%in%"Nointeract"&
qval<qval.ct & check.var%in%check.var.use & value%in%c(check.var.use) & !value%in%exclude.val)
ses.sig.f <- unique(c(ses.sp.int$feature))
ses.sp.main <- unique(ses.sp.main$feature)[!unique(ses.sp.main$feature)%in%ses.sig.f]
ses.sp.long <- subset(SES.masslin.all.use, check.var%in%check.var.use & feature%in%unique(c(ses.sig.f,ses.sp.main)))
#Create the pattern of change
ses.sp.long.pattern <- ses.sp.long
ses.sp.long.pattern$coef[ses.sp.long.pattern$qval>0.1 &ses.sp.long.pattern$value!="exact_age" ] <- 0
sp.increase <- subset(SES.masslin.all.use, value=="exact_age" & coef>0 & check.var%in%check.var.use & Model=="Interact")$feature
sp.decrease <- subset(SES.masslin.all.use, value=="exact_age" & coef<0 & check.var%in%check.var.use & Model=="Interact")$feature
pattern.type <- dcast(ses.sp.long.pattern[,c("feature","value","coef","Model")], Model+ feature ~ value, value.var = c("coef"))
pattern.type <- pattern.type[order(pattern.type$Model),]
pattern.type$time_trend <- ifelse(pattern.type$exact_age>0,"Increase",
ifelse(pattern.type$exact_age<0,"Decrease","No change"))
pattern.type$slope_SES <- ifelse(is.na(pattern.type$time_interaction),"No change",
ifelse(pattern.type$time_interaction>0,"High",
ifelse(pattern.type$time_interaction<0,"Low","No change")))
pattern.type$baseline_SES <- ifelse(pattern.type$MR1_latent.raw>0,"High",
ifelse(pattern.type$MR1_latent.raw<0,"Low","No change"))
pattern.type.1 <- subset(pattern.type,slope_SES=="No change" & feature%in%ses.sp.main & Model=="Nointeract")
pattern.type.2 <- subset(pattern.type,slope_SES!="No change" & feature%in%ses.sig.f & Model=="Interact")
pattern.type <- rbind(pattern.type.1,pattern.type.2)
pattern.type$pattern <- ifelse(pattern.type$slope_SES=="No change", paste(pattern.type$time_trend, pattern.type$baseline_SES),
paste(pattern.type$time_trend, pattern.type$baseline_SES, pattern.type$slope_SES, sep="_"))
table(pattern.type$pattern)
ses.sp.long <- merge(ses.sp.long, pattern.type[,c("feature","pattern")], by="feature",all.x=T)
ses.sp.long$sig <- ifelse(ses.sp.long$qval<0.1,"sig","nosig")
ses.sp.long$Effect <- ifelse(ses.sp.long$value=="MR1_latent.raw"& ses.sp.long$Model=="Interact", "Baseline",
ifelse(ses.sp.long$value=="MR1_latent.raw"& ses.sp.long$Model=="Nointeract", "Overall",
ifelse(ses.sp.long$value=="time_interaction", "Slope","Time")))
table(ses.sp.long$pattern)
ses.sp.long$pattern <- factor(ses.sp.long$pattern, levels=c("Increase High" ,
"Increase Low" ,
"Increase_No change_High",
"Increase_No change_Low",
"Increase_Low_High" ,
"Increase_High_Low" ,
"Decrease_Low_High" ,
"Decrease_High_Low"),
ordered=T)
ses.sp.long$Direction <- ifelse(ses.sp.long$coef>0,"Positive","Negative")
sp.order.ses <- ses.sp.long[ses.sp.long$value=="MR1_latent.raw",]
sp.order.ses <- sp.order.ses[order(abs(sp.order.ses$coef)),]
sp.order.ses <- sp.order.ses[order(sp.order.ses$Model,decreasing=F),]
ses.sp.long$feature_update <- factor(ses.sp.long$feature_update,levels=unique(sp.order.ses$feature_update), ordered=T)
ses.sp.long$Effect <- factor(ses.sp.long$Effect, levels=c("Baseline","Slope","Overall","Time"), ordered=T)
ses.sp.long$Model <- ifelse(ses.sp.long$Model=="Interact","Model with interaction", "Model without interaction")
ses.plot <- ggplot(subset(ses.sp.long ,Effect !="Time" ),
aes(x=coef, y=feature_update,fill=Direction,alpha=sig)) +
theme_bw() +
xlab("Coefficient") +
geom_errorbar(aes(xmin=coef-1.96*stderr,xmax=coef+1.96*stderr), width=.2, position=position_dodge(0.5)) +
geom_point(position=position_dodge(0.5), size = 3, pch = 21) +
facet_grid(pattern~Model+Effect,scale="free_y",space="free_y") +
ylab("") + ggtitle("") +
xlab("") + xlab("MaAsLin2 coefficient") + ylab("") +
scale_shape_manual(values=seq(0,10)) +
scale_fill_manual(values = c(
"black","black" ))+
scale_alpha_discrete(range = c(0.5,1))+
geom_vline(xintercept=0)
pdf("Figures/P5 Maaslin2 Species SES.pdf", height=8, width=10)
plot(ses.plot)
dev.off()
sig.sp.ses <- unique(ses.sp.long$feature)
save(sig.sp.ses, file="Results/P5 Maaslin2 sig ses species mclr.RData")Generate figure of species assocaited with SES (Figure S3A)
plot(ses.plot)
Species assocaited with early-life factors
ff.order <- c(layer_scales(ses.plot, 1, 2)$y$range$range,
layer_scales(ses.plot, 2, 2)$y$range$range,
layer_scales(ses.plot, 3, 2)$y$range$range,
layer_scales(ses.plot, 4, 2)$y$range$range,
layer_scales(ses.plot, 5, 2)$y$range$range,
layer_scales(ses.plot, 6, 2)$y$range$range,
layer_scales(ses.plot, 7, 2)$y$range$range,
layer_scales(ses.plot, 8, 2)$y$range$range)
######################################################################
#Longitudinal analysis results for SES & early life factors
################################################################################
check.var.use <- unique(SES.maaslin.combine$check.var)
used.vars <- c("MR1_latent.raw","atbx.microbiome.new", "bf","EBF_6m",
"atbx.m.birth", "atbx.m.preg", "awayhome1h_1y" ,
"awayhome1h_3m","bw_sd", "canue_Zdweldensity_pre18w" ,
"cat_1y", "condition_GestationalDiabetes_mother_prelabour",
"dog_1y", "fasthma","father_atopy_1y",
"freq_score_basic", "gest_days", "hometype_3m","masthma","maternaldistress_perceived_1y",
"maternaldistress_perceived_pre18w", "mom_bmi_best", "mom_UPFContr", "mother_atopy_1y",
"no2.preg", "no2.y1", "num_siblings", "postnatal_smoke",
"Pregnancy.Tdens250", "prenatal_smoke", "rural", "furniture_densityclutter_3m",
"home_plant_3m", "floor_carpet_3m", "mould_3m_mh", "vaginal", "fall","summer", "winter")
ses.sig.f.all <- unique(c(ses.sp.main,ses.sig.f))
all.sp <- subset(SES.masslin.all.use, !value%in%exclude.val & feature%in%ses.sig.f.all & check.var%in%used.vars)
all.sp$U <- ifelse(all.sp$qval<0.1,"*","")
all.sp$Effect <- ifelse(all.sp$value=="time_interaction","Slope",
ifelse(all.sp$Model=="Interact", "Baseline",
ifelse(all.sp$Model=="Nointeract", "Overall",NA)))
all.sp <- merge(all.sp, pattern.type[,c("feature","pattern")], by="feature")
all.sp$pattern <- factor(all.sp$pattern, levels=c("Increase High" ,
"Increase Low" ,
"Increase_No change_High",
"Increase_No change_Low",
"Increase_Low_High" ,
"Increase_High_Low" ,
"Decrease_Low_High" ,
"Decrease_High_Low"), ordered=T)
all.sp <- merge(all.sp, data.map, by.x="check.var", by.y="Vars",all.x=T)
all.sp.use <- subset(all.sp, Category!="Health")
all.sp.use$Category <- factor(all.sp.use$Category, levels=c("SES","Pregnancy",
"Birth",
"Postnatal",
"Home Environment",
"Neighbourhood Environment",
"Parental Diet/Health"),
ordered=T)
all.sp.use$Effect <- factor(all.sp.use$Effect, levels=c("Baseline", "Slope","Overall"), ordered=T)
all.sp.use$feature_update <- factor(all.sp.use$feature_update,
levels=c(ff.order), ordered=T)
all.sp.update <- NULL
for(check.var.i in unique(all.sp.use$check.var)){
ff.main <- subset(all.sp.use, check.var == check.var.i & Model=="Nointeract" & Effect=="Overall")
ff.int <- subset(all.sp.use, check.var == check.var.i & Model=="Interact" & Effect=="Slope")
ff.all <- rbind(ff.main,ff.int)
all.sp.update <- rbind(all.sp.update, ff.all)
}
all.sp.use.sig <- unique(subset(all.sp.update, qval<0.1, select=c("check.var","feature_update","Label","Category","pattern")))
all.sp.use.sig$feature_update <- factor(all.sp.use.sig$feature_update,
levels=c(ff.order), ordered=T)
sig.all.barplot <- ddply(all.sp.use.sig, .(Label,Category), summarise, N=length(feature_update))
sig.all.barplot2 <- ddply(subset(all.sp.use.sig, Label!="SES"),.(feature_update,pattern,Category),
summarise, N=length(feature_update))
sig.all.barplot2.sum <- ddply(sig.all.barplot2, .(feature_update,pattern),
summarise, All=length(feature_update))
sig.all.barplot2 <- merge(sig.all.barplot2,sig.all.barplot2.sum,by=c("feature_update","pattern"),all = T )
sig.all.barplot2$Percent <- sig.all.barplot2$N/sig.all.barplot2$All
df2 <- subset(all.sp.use.sig, Label!="SES Index") %>%
group_by(feature_update, pattern,Category) %>%
dplyr::summarise(n=n())%>%
mutate(percent = (n / sum(n)), cumsum = cumsum(percent),N=n)
df2$Category <- factor(df2$Category, levels=c("Pregnancy",
"Birth",
"Postnatal",
"Home Environment",
"Neighbourhood Environment",
"Parental Diet/Health"),
ordered=T)
sig.all.barplot <- sig.all.barplot[order(sig.all.barplot$N,decreasing=T),]
sig.all.barplot$Label <- factor(sig.all.barplot$Label, levels=unique(sig.all.barplot$Label), ordered = T)
all.sp.use.sig$Label <- factor(all.sp.use.sig$Label, levels=unique(sig.all.barplot$Label), ordered = T)
df3 <- ddply(df2,.(feature_update),summarise, N=sum(n))
df3 <- df3[order(df3$N),]
df2$feature_update <- factor(df2$feature_update, levels=unique(df3$feature_update), ordered=T)
all.sp.use.sig$feature_update <- factor(all.sp.use.sig$feature_update, levels=unique(df3$feature_update), ordered=T)
gg1 <- ggplot(data=df2, aes(x=n, y=feature_update,fill = Category)) +
geom_bar(stat="identity") +
scale_fill_manual(values=c( "#E9C46A","#F4A261", "#E76F51", "#2A9D8F", "#287271", "#9A4770"))+
facet_grid(pattern~.,scales = "free",space = "free") +
theme_void() +
theme(legend.position = "none", strip.background = element_blank(),
strip.text.x = element_blank())
gg2 <- ggplot(data=sig.all.barplot, aes(x=Label, y=N,fill=Category)) +
geom_bar(stat="identity")+
geom_text(aes(label=N), vjust=1.6, color="white", size=3.5)+
scale_fill_manual(values=c( "#8AB17D", "#E9C46A","#F4A261", "#E76F51", "#2A9D8F", "#287271", "#9A4770"))+
facet_grid(~Category,scales = "free",space = "free") +
theme_void() +
theme(legend.position = "none", strip.background = element_blank(),
strip.text.x = element_blank())
gg3 <- ggplot(subset(all.sp.use.sig),
aes(y=feature_update, x=Label, fill=Category)) +
geom_point(pch=21,size=2) +
theme_bw() + ylab("") + xlab("")+ theme(legend.position="bottom")+
facet_grid(pattern~Category,scales = "free",space = "free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
strip.text.x = element_blank()) +
scale_fill_manual(values=c( "#8AB17D", "#E9C46A","#F4A261", "#E76F51", "#2A9D8F", "#287271", "#9A4770"))
sig.sp.bf <- as.character(unique(subset(all.sp.use.sig, check.var%in%c("bf","EBF_6m"))$feature_update))
save(sig.sp.ses, sig.sp.bf,file="Results/P5 Maaslin2 sig ses species mclr.RData")Generate figures of species assocaited SES and early-life factors (Figure 4A)
plot(gg1)
plot(gg2)
plot(gg3)
pdf("Figures/P5 Maaslin2 Species all exposomes simple.pdf", height=9, width=10)
plot(gg1)
plot(gg2)
plot(gg3)
dev.off()## quartz_off_screen
## 2Generate bubble figure to compare the effect of BF and SES (Figure 4B)
######################################################################
#Longitudinal analysis results for SES & BF narrow down to focus - Bubble plot
################################################################################
bf.sig.use.main <- unique(c(subset(SES.masslin.all.use,
qval<qval.ct & Model=="Nointeract"&
value%in%c("MR1_latent.raw","bf") &
!value%in%exclude.val )$feature))
bf.sig.use.slope <- unique(subset(SES.masslin.all.use,
qval<qval.ct & Model=="Interact"&
check.var%in%c("MR1_latent.raw","bf") & value=="time_interaction"&
!value%in%exclude.val )$feature)
all.sp.bf <- rbind(subset(SES.masslin.all.use, !value%in%exclude.val & feature%in%bf.sig.use.slope & check.var%in%c("MR1_latent.raw","bf") & value=="time_interaction" & Model=="Interact"),
subset(SES.masslin.all.use, !value%in%exclude.val & feature%in%bf.sig.use.main & check.var%in%c("MR1_latent.raw","bf") & value%in%c("MR1_latent.raw","bf")& Model=="Nointeract"))
all.sp.bf$U <- ifelse(all.sp.bf$qval<0.1,"*","")
all.sp.bf$Effect <- ifelse(all.sp.bf$value=="time_interaction","Slope",
ifelse(all.sp.bf$Model=="Interact", "Baseline",
ifelse(all.sp.bf$Model=="Nointeract", "Overall",NA)))
all.sp.bf <- merge(all.sp.bf, data.map, by.x="check.var", by.y="Vars",all.x=T)
all.sp.bf$Effect <- factor(all.sp.bf$Effect, levels=c( "Slope","Overall"), ordered=T)
all.sp.bf$Label[all.sp.bf$Label=="SES Index"] <- "SES"
all.sp.bf$Label <- factor(all.sp.bf$Label, levels=c( "SES","Breastfeeding"), ordered=T)
all.sp.bf$Direction <- ifelse(all.sp.bf$coef>0,"Positive","Negative")
all.sp.bf$sig <- ifelse(all.sp.bf$qval<0.1,"sig","nosig")
#Dot plot
all.sp.bf.wide <- reshape2::dcast(all.sp.bf, feature +Effect ~ Label, value.var = "coef")
#To play add the mean relative
data.sp <- as.matrix(otu_table(RBNME_species_MCLR))
data.meta <- data.frame(sample_data(RBNME_species_MCLR))
data.sp.3m <- as.matrix(otu_table(subset_samples(RBNME_species_MCLR, Visit=="3 month")))
data.sp.1y <- as.matrix(otu_table(subset_samples(RBNME_species_MCLR, Visit=="1 year")))
rb.data <- merge(merge(data.frame(total_mean=apply(data.sp,2,mean)),
data.frame(m3_mean=apply(data.sp.3m,2,mean)), by="row.names"),
data.frame(y1_mean=apply(data.sp.1y,2,mean)),by.x="Row.names", by.y="row.names")
rb.data$Row.names <- gsub(" ","\\.",rb.data$Row.names)
all.sp.bf.wide <- merge(all.sp.bf.wide, rb.data, by.x="feature", by.y="Row.names",all.x=T)
data.sp.tax <- as.matrix(tax_table(RBNME_species_MCLR))
rownames(data.sp.tax) <- gsub(" ","\\.",rownames(data.sp.tax))
all.sp.bf.wide <- merge(all.sp.bf.wide,data.sp.tax ,by.x="feature", by.y="row.names")
all.sp.bf.wide$Family <- gsub("f__","",all.sp.bf.wide$Family)
all.sp.bf.wide$Order <- gsub("o__","",all.sp.bf.wide$Order)
sesbf.p <- ggplot(data=all.sp.bf.wide, aes(x=SES, y=Breastfeeding)) +
geom_hline(yintercept = 0, color="grey")+
geom_vline(xintercept = 0, color="grey")+
geom_point(aes(size=total_mean,color=Order)) + #
geom_smooth(method="lm", color="black")+
scale_color_cosmic()+
theme(aspect.ratio = 1)+
facet_grid(~Effect,scale="free",space="free_y")+
theme_bw()+
stat_cor( geom = "label")
plot(sesbf.p)## `geom_smooth()` using formula = 'y ~ x'
pdf("Figures/P5 Maaslin2 Bubble figure for SES and BF.pdf", height=4, width=9)
plot(sesbf.p)## `geom_smooth()` using formula = 'y ~ x'dev.off()## quartz_off_screen
## 2Generate heatmap of associaiton between early-life factors and microbiome (Figure S3B)
######################################################################
#Supplementary figure
all.sp.use$Label <- factor(all.sp.use$Label, levels=levels(all.sp.use.sig$Label), ordered=T)
gg <- ggplot(subset(all.sp.use),
aes(y=feature_update, x=Label)) +
geom_tile(aes(fill=coef)) +
geom_text(aes(label = U), color = "white", size = 4) +
scale_fill_continuous_divergingx(palette = 'RdBu', mid = 0, l3 = 0, p1=0.5,p2=0.4,p3 = .1, p4 = .5,
name="Coefficient",rev = T, na.value = "white") +
theme_bw() + ylab("") + xlab("")+ theme(legend.position="bottom") +
facet_grid(pattern~Category+Effect,scales = "free",space = "free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
gg2 <- ggplot(subset(all.sp.use,Effect%in%c("Overall","Slope")),
aes(y=feature_update, x=Label)) +
geom_tile(aes(fill=coef)) +
geom_text(aes(label = U), color = "white", size = 4) +
scale_fill_continuous_divergingx(palette = 'RdBu', mid = 0, l3 = 0, p1=0.5,p2=0.4,p3 = .1, p4 = .5,
name="Coefficient",rev = T, na.value = "white") +
theme_bw() + ylab("") + xlab("")+ theme(legend.position="bottom") +
facet_grid(pattern~Category+Effect,scales = "free",space = "free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
plot(gg2)
pdf("Figures/P5 Maaslin2 Species all.pdf", height=9, width=15)
plot(gg2)
dev.off()## quartz_off_screen
## 2Species associated with phenotypes (Figure 5B)
phhe.sp <- subset(SES.masslin.all.use, qval<qval.ct &
check.var%in%c("Tscore_abnormal",
"asthma5y_ep","atopy5y", "overweight") &
!value%in%exclude.val )
phhe.sp$Effect <- ifelse(phhe.sp$value=="time_interaction","Slope",
ifelse(phhe.sp$Model=="Interact", "Baseline",
ifelse(phhe.sp$Model=="Nointeract", "Overall",NA)))
phhe.sp.update <- NULL
for(check.var.i in unique(phhe.sp$check.var)){
ff.main <- subset(phhe.sp, check.var == check.var.i & Model=="Nointeract" & Effect=="Overall")
ff.int <- subset(phhe.sp, check.var == check.var.i & Model=="Interact" & Effect=="Slope")
ff.all <- rbind(ff.main,ff.int)
phhe.sp.update <- rbind(phhe.sp.update, ff.all)
}
phe.sig.use <- unique(phhe.sp.update$feature)
all.sp.phe <- subset(SES.masslin.all.use, !value%in%exclude.val &
feature%in%phe.sig.use &
check.var%in%c("MR1_latent.raw",
"Tscore_abnormal",
"asthma5y_ep","atopy5y", "overweight"))
all.sp.phe$U <- ifelse(all.sp.phe$qval<0.1,"*","")
all.sp.phe$Effect <- ifelse(all.sp.phe$value=="time_interaction","Slope",
ifelse(all.sp.phe$Model=="Interact", "Baseline",
ifelse(all.sp.phe$Model=="Nointeract", "Overall",NA)))
all.sp.phe <- merge(all.sp.phe, data.map, by.x="check.var", by.y="Vars",all.x=T)
all.sp.phe <- subset(all.sp.phe, Effect!="Baseline")
all.sp.phe$Effect <- factor(all.sp.phe$Effect, levels=c("Slope","Overall"), ordered=T)
all.sp.phe$Category <- factor(all.sp.phe$Category, levels=c("SES","Health"),
ordered=T)
all.sp.phe$Label[all.sp.phe$Label=="Overweight at 5y" ] <- "Overweight/obese"
all.sp.phe$Label[all.sp.phe$Label=="Abnormal child behavior at 5y" ] <-"Abnormal child behavior"
all.sp.phe$Label[all.sp.phe$Label=="Atopy at 5y"] <-"Atopy"
all.sp.phe$Label[all.sp.phe$Label=="Aasthma at 5y" ] <-"Asthma"
all.sp.phe$Label <- factor(all.sp.phe$Label, levels=c( "SES Index","Overweight/obese", "Asthma","Atopy",
"Abnormal child behavior" ),
ordered=T)
all.sp.phe$SES <- ifelse(all.sp.phe$feature_update%in%ff.order, "Associated with SES", "Not associated with SES")
data.cc <- dcast(all.sp.phe, feature_update~Label+Effect,value.var = "coef")
data.cc[is.na(data.cc)] <- 0
rownames(data.cc) <- data.cc$feature_update
data.cc <- data.cc[,-1]
ord <- hclust( dist((data.cc), method = "euclidean"), method = "ward.D" )$order
sp.cc <- rownames(data.cc[ord,])
all.sp.phe.sig <- unique(subset(all.sp.phe, qval<0.1, select=c("check.var","feature_update","Label","Category","SES")))
save(sig.sp.ses, sig.sp.bf,all.sp.phe.sig,file="Results/P5 Maaslin2 sig ses species mclr.RData")
all.sp.phe.sig$feature_update <- factor(all.sp.phe.sig$feature_update,
levels=c(sp.cc), ordered=T)
sig.all.barplot <- ddply(all.sp.phe.sig, .(Label,Category,SES), summarise,
N=length(feature_update))
sig.all.barplot2 <- ddply(subset(all.sp.phe.sig,check.var!="MR1_latent.raw"),
.(feature_update,Category,SES), summarise, N=length(feature_update))
sig.all.barplot$SES <- factor(sig.all.barplot$SES, levels=c( "Not associated with SES" , "Associated with SES" ),
ordered=T)
sig.all.barplot$Text <- NA
sig.all.barplot$Text[sig.all.barplot$Label!="SES" & sig.all.barplot$SES=="Associated with SES"] <- sig.all.barplot$N[sig.all.barplot$Label!="SES" & sig.all.barplot$SES=="Associated with SES"]
sig.all.barplot2 <- sig.all.barplot2[order(sig.all.barplot2$SES,decreasing = T),]
sig.all.barplot2 <- sig.all.barplot2[order(sig.all.barplot2$N),]
sig.all.barplot2$feature_update <- factor(sig.all.barplot2$feature_update, levels=unique(sig.all.barplot2$feature_update), ordered=T)
all.sp.phe.sig$feature_update <- factor(all.sp.phe.sig$feature_update, levels=unique(sig.all.barplot2$feature_update), ordered=T)
library( "MetaNet")
all.sp.phe.sig$value <- 1
tab <- dcast(all.sp.phe.sig, feature_update~Label,value.var = "value")
tab[is.na(tab)] <- 0
rownames(tab) <- tab$feature_update
tab <- tab[,-1]
venn_net(tab) -> v_net
plot(v_net)
pdf("Figures/venn.pdf")
plot(v_net)
dev.off()## quartz_off_screen
## 2Plot B.infantis vs. SES and BF (Figure 5C,D)
###################################################################################################
check.var.use <- c("MR1_latent.raw","Tscore_abnormal","asthma5y_ep", "overweight","atopy5y")
names(check.var.use) <- c("SES", "Abnormal child behavior","Asthma", "Overweight/obese","Atoppy")
phe.sp <- subset(SES.masslin.all, Normalization=="MCLR" &
adjbasic=="ProcessingPeriod + exact_age" &
qval<qval.ct & check.var%in%check.var.use[check.var.use!="MR1_latent.raw"] &
!value%in%exclude.val & feature%in%unique(c(ses.sig.f,ses.sp.main)))
phe.sig.f <- unique(phe.sp$feature)
overweight.f <- unique(phe.sp$feature[phe.sp$check.var=="overweight"])
asthma.f <- unique(phe.sp$feature[phe.sp$check.var=="asthma5y_ep"])
tscore.f <- unique(phe.sp$feature[phe.sp$check.var=="Tscore_abnormal"])
atopy.f <- unique(phe.sp$feature[phe.sp$check.var=="atopy5y"])
data.sp <- data.frame(otu_table(RBNME_species_MCLR))
# data.sp[data.sp==0] <- min(data.sp[data.sp!=0])/2
# data.sp <- log(data.sp)
data.meta <- data.frame(sample_data(RBNME_species_MCLR))
data.sp.use <- merge(data.meta, data.sp[,phe.sig.f,drop=F], by.x="SampleID", by.y="row.names")
data.sp.use <- merge(data.withmissing, data.sp.use,by.x="subjectnumber", by.y="SubjectNumber")
data.sp.use <- subset(data.sp.use, exact_age<1.5)
data.sp.use$atbx.microbiome <- ifelse(data.sp.use$atbx.1y.time<data.sp.use$exact_age*365,1,0)
data.sp.use$atbx.microbiome.new <- ifelse(data.sp.use$Visit=="3 month", as.numeric(as.character(data.sp.use$atbx.micro3m)),
as.numeric(as.character(data.sp.use$atbx.micro1y)))
data.sp.use$bf <- ifelse(data.sp.use$exact_age*12<data.sp.use$bf_duration_imp,1, 0)
data.sp.use.plot <- data.sp.use[,c("subjectnumber","SampleID", "exact_age","cat_SES","bfhistory_f","bfhistory_m","B2","bf",check.var.use, phe.sig.f)]
# ggplot(subset(data.sp.use.plot,exact_age<0.5 & bf==1), aes(log(B2),Bifidobacterium_longum.subsp.infantis)) +
# geom_point()
data.sp.use.plot.long <- melt(data.sp.use.plot, id.vars = c("subjectnumber","SampleID", "exact_age",check.var.use,"cat_SES",
"bfhistory_f","bfhistory_m","B2"))
data.sp.use.plot.long1 <- subset(data.sp.use.plot.long, select=c("subjectnumber","SampleID","exact_age","variable","value","cat_SES"))
names(data.sp.use.plot.long1) <- c("subjectnumber","SampleID","exact_age","variable","value","phenotype")
data.sp.use.plot.long2 <- subset(data.sp.use.plot.long,variable%in%asthma.f, select=c("subjectnumber","SampleID","exact_age","variable","value","asthma5y_ep"))
data.sp.use.plot.long2$asthma5y_ep <- ifelse(data.sp.use.plot.long2$asthma5y_ep==1,"Asthma","No asthma")
names(data.sp.use.plot.long2) <- c("subjectnumber","SampleID","exact_age","variable","value","phenotype")
data.sp.use.plot.long3 <- subset(data.sp.use.plot.long,variable%in%overweight.f , select=c("subjectnumber","SampleID","exact_age","variable","value","overweight"))
data.sp.use.plot.long3$overweight <- ifelse(data.sp.use.plot.long3$overweight==1,"Overweight/obese","Normal weight")
names(data.sp.use.plot.long3) <- c("subjectnumber","SampleID","exact_age","variable","value","phenotype")
data.sp.use.plot.long5 <- subset(data.sp.use.plot.long, variable%in%tscore.f , select=c("subjectnumber","SampleID","exact_age","variable","value","Tscore_abnormal"))
data.sp.use.plot.long5$Tscore_abnormal <- ifelse(data.sp.use.plot.long5$Tscore_abnormal==1,"Abnormal child behavior",
"Normal child behavior")
names(data.sp.use.plot.long5) <- c("subjectnumber","SampleID","exact_age","variable","value","phenotype")
data.sp.use.plot.long6 <- subset(data.sp.use.plot.long, variable%in%atopy.f , select=c("subjectnumber","SampleID","exact_age","variable","value","atopy5y"))
data.sp.use.plot.long6$atopy5y <- ifelse(data.sp.use.plot.long6$atopy5y==1,"Atopy",
"No Atopy")
names(data.sp.use.plot.long6) <- c("subjectnumber","SampleID","exact_age","variable","value","phenotype")
data.sp.use.plot.long1$Category <- "SES"
data.sp.use.plot.long2$Category <- "Asthma"
data.sp.use.plot.long3$Category <- "Overweight/obese"
data.sp.use.plot.long5$Category <- "Abnormal Child Behavior"
data.sp.use.plot.long6$Category <- "Atopy"
data.sp.use.plot.long.all <- rbind(data.sp.use.plot.long1,data.sp.use.plot.long2,data.sp.use.plot.long3,data.sp.use.plot.long5,data.sp.use.plot.long6)
data.sp.use.plot.long.all$Category <- factor(data.sp.use.plot.long.all$Category, levels=c("SES","Overweight/obese","Asthma","Atopy","Abnormal Child Behavior"), ordered=T)
data.sp.use.plot.long.all <- merge(data.sp.use.plot.long.all, pattern.type[,c("feature","pattern")], by.x="variable", by.y="feature")
data.sp.use.plot.long.all$pattern <- factor(data.sp.use.plot.long.all$pattern, levels=c("Increase_High_No change","Increase_Low_No change",
"Increase_No change_High", "Increase_No change_Low",
"Increase_Low_High", "Increase_High_Low",
"Decrease_High_Low", "Decrease_Low_High"), ordered=T)
unique(data.sp.use.plot.long.all$variable)## [1] Bacteroides_caccae.SGB1877
## [2] Bifidobacterium_dentium.SGB17234
## [3] Bifidobacterium_longum.subsp.infantis
## [4] Clostridium_innocuum.SGB4037
## [5] Clostridium_sp_C5_48.SGB4752
## [6] Eggerthella_lenta.SGB14809
## [7] Faecalibacterium_prausnitzii.SGB15316
## [8] Megasphaera_micronuciformis.SGB5868
## [9] Phocaeicola_dorei.SGB1815
## [10] Ruminococcus_gnavus.SGB4584
## [11] Ruminococcus_torques.SGB4608
## [12] Ruthenibacterium_lactatiformans.SGB15271
## [13] Streptococcus_mitis.SGB8168
## [14] Streptococcus_salivarius.SGB8005
## [15] Streptococcus_salivarius.SGB8007_group
## [16] Streptococcus_thermophilus.SGB8002
## 17 Levels: bf ... Eggerthella_lenta.SGB14809data.sp.use.plot.long.all <- merge(data.sp.use.plot.long.all, data.withmissing[,c("subjectnumber","bf_duration_imp")],by="subjectnumber",all.x=T)
data.sp.use.plot.long.all$BF <- ifelse(data.sp.use.plot.long.all$exact_age*12>data.sp.use.plot.long.all$bf_duration_imp,"Children without BF",
"Children with BF")
data.sp.use.plot.long.all2 <- data.sp.use.plot.long.all
data.sp.use.plot.long.all2$BF <- "All children"
data.sp.use.plot.long.all <- rbind(data.sp.use.plot.long.all2, data.sp.use.plot.long.all)
Binf.p <- ggplot(subset(data.sp.use.plot.long.all, !is.na(phenotype) &variable%in%c("Bifidobacterium_longum.subsp.infantis")& Category=="SES" &
!is.na(BF)),
aes(exact_age, value, group=phenotype,fill=phenotype,col=phenotype)) +
geom_smooth(method="lm") +
facet_wrap(~BF,ncol=3) +
theme_bw()+
scale_fill_manual(values=c("#C3D7BD","#A4C19A","#5D836E","#3F615E")) +
scale_color_manual(values=c("#C3D7BD","#A4C19A","#5D836E","#3F615E")) +
xlab("Child age (yr)") +
ylab("Relative abundance (mclr) of B. infantis")
plot(Binf.p)## `geom_smooth()` using formula = 'y ~ x'
pdf("Figures/P5 phenotype sig sp.pdf", width=8, height=6)
plot(Binf.p)## `geom_smooth()` using formula = 'y ~ x'dev.off()## quartz_off_screen
## 2#Prevalence figure
data.binf.prev <- subset(data.sp.use.plot.long.all, !is.na(phenotype) &variable%in%c("Bifidobacterium_longum.subsp.infantis")& Category=="SES" & !is.na(BF))
data.binf.prev <- merge(data.binf.prev, data.div[,c("SampleID","Visit")],by=c("SampleID"),all.x=T)
data.binf.prev.s <- ddply(data.binf.prev, .(Visit,BF,phenotype,variable), summarise,
prev=length(which(value!=0))/length(value)*100)
data.binf.prev.s$Visit <- factor(data.binf.prev.s$Visit, levels=c("3 month", "1 year"), ordered=T)
Binf.prev <- ggplot(data.binf.prev.s,
aes(Visit, prev, group=phenotype,fill=phenotype,col=phenotype)) +
geom_bar(stat="identity",position = "dodge") +
facet_wrap(~BF,ncol=3) +
theme_bw()+
scale_fill_manual(values=c("#C3D7BD","#A4C19A","#5D836E","#3F615E","#61C6E4")) +
scale_color_manual(values=c("#C3D7BD","#A4C19A","#5D836E","#3F615E","#61C6E4")) +
xlab("Visit") +
ylab("Prevalence of B. infantis (%)")+
geom_text(aes(label=paste0(round(prev,0),"%")), color="black", size=3.5,
position=position_dodge(width=0.9), vjust=0.1)
plot(Binf.prev)
pdf("Figures/P5 phenotype sig sp prev.pdf", width=8, height=6)
plot(Binf.prev)
dev.off()## quartz_off_screen
## 2ss.binfantis <- unique(data.sp.use.plot.long.all$subjectnumber[data.sp.use.plot.long.all$value!=0 & data.sp.use.plot.long.all$variable=="Bifidobacterium_longum.subsp.infantis"])
ss.binfantis.no <- names(which(table(data.sp.use.plot.long.all$subjectnumber[data.sp.use.plot.long.all$value==0 & data.sp.use.plot.long.all$variable=="Bifidobacterium_longum.subsp.infantis"])==16))
save(ss.binfantis, file="Results/subjects with Binfantis.RData")bf history vs. B.infantis (Figure S5, Figure 5F)
#Breastfeeding history vs. B. infantis prevalence
data.withmissing$Binfantis.pre <- ifelse(data.withmissing$subjectnumber%in%ss.binfantis, 1,
ifelse(data.withmissing$subjectnumber%in%ss.binfantis.no, 0,NA))
prop.table(table(data.withmissing$Binfantis.pre))##
## 0 1
## 0.7462149 0.2537851data.withmissing$BF <- ifelse(data.withmissing$bf_duration_imp>=3,1,0)
data.bf.plot <- data.frame(bf.prev=prop.table(with(data.withmissing, table(BF,site)),margin = 2)[2,])
data.bf.plot$Site <- rownames(data.bf.plot)
data.bf.plot$bf.prev <- data.bf.plot$bf.prev*100
library(Hmisc)
data.bf.plot$Site <- capitalize(data.bf.plot$Site)
data.bf.plot$Site <- factor(data.bf.plot$Site , levels=c("Vancouver","Edmonton" ,"Winnipeg" , "Toronto" ), ordered=T)
bfr.plot <- ggplot(data.bf.plot,aes(y=bf.prev, x=Site,fill = "#e76f51")) +
theme_bw() +
geom_bar(stat="identity") +
geom_text(aes(label = paste0(round(bf.prev,0),"%")))+
ylab("Breastfeeding rates (%)") + ggtitle("") +
xlab("") + ylim(c(0,100))
bfr.plot
pdf("Figures/BF rate.pdf")
bfr.plot
dev.off()## quartz_off_screen
## 2used.vars.binfantis <- c(used.vars, "bfhistory_m","bfhistory_f")
used.vars.binfantis[used.vars.binfantis=="atbx.microbiome.new"] <- "atbx.1y"
used.vars.binfantis <- used.vars.binfantis[used.vars.binfantis!="bf"]
data.withmissing.bb <- data.withmissing
RES.bb.all <- NULL
for(used.var.binfantis in used.vars.binfantis){
data.withmissing.bb$Var <- scale(as.numeric(as.character(data.withmissing.bb[,used.var.binfantis])))
fit.bb <- glm(Binfantis.pre ~ Var + male + site, family="binomial", subset(data.withmissing.bb,BF==1))
coef.bb <- as.data.frame(summary(fit.bb)$coef)
res.bb <- coef.bb[2,c("Estimate", "Pr(>|z|)"),drop=F]
res.bb$low_ci <- confint(fit.bb)[2,1]
res.bb$high_ci <- confint(fit.bb)[2,2]
res.bb$Var <- used.var.binfantis
RES.bb.all <- rbind(RES.bb.all, res.bb)
}
RES.bb.all$site <- "All"
RES.bb <- NULL
for(used.var.binfantis in used.vars.binfantis){
for(site.use in unique(data.withmissing.bb$site)){
data.withmissing.bb$Var <- scale(as.numeric(as.character(data.withmissing.bb[,used.var.binfantis])))
fit.bb <- glm(Binfantis.pre ~ Var + male, family="binomial", subset(data.withmissing.bb,BF==1 & site==site.use))
coef.bb <- as.data.frame(summary(fit.bb)$coef)
res.bb <- coef.bb[2,c("Estimate", "Pr(>|z|)"),drop=F]
res.bb$low_ci <- confint(fit.bb)[2,1]
res.bb$high_ci <- confint(fit.bb)[2,2]
res.bb$Var <- used.var.binfantis
res.bb$site <- site.use
RES.bb <- rbind(RES.bb, res.bb)
}
}
RES.bb.full <- rbind(RES.bb.all, RES.bb)
range(RES.bb.full$Estimate,na.rm=T)## [1] -5.1302515 0.7938585range(RES.bb.full$low_ci,na.rm=T)## [1] -1.3185251 0.2094367range(RES.bb.full$high_ci,na.rm=T)## [1] -0.1417484 39.2997000RES.bb.full[RES.bb.full$Estimate < -10,"high_ci"] <- NA
RES.bb.full[RES.bb.full$Estimate < -10,"Estimate"] <- NA
RES.bb.full[RES.bb.full$Estimate > 10& !is.na(RES.bb.full$Estimate) ,"low_ci"] <- NA
RES.bb.full[RES.bb.full$Estimate > 10 & !is.na(RES.bb.full$Estimate),"Estimate"] <- NA
#
# RES.bb.full[RES.bb.full$high_ci >5,"Estimate"] <- NA
# RES.bb.full[RES.bb.full$high_ci >5,"high_ci"] <- NA
data.map[data.map$Vars=="bfhistory_m",]## Vars Category Label
## bfhistory_m bfhistory_m Breastfeeding history Mother was breastfed as an infant
## Type Values
## bfhistory_m Categorical <NA>RES.bb.use <- merge(RES.bb.full, data.map, by.x="Var", by.y="Vars",all.x=T)
RES.bb.use$Category <- factor(RES.bb.use$Category, levels=c("SES","Pregnancy",
"Birth",
"Postnatal",
"Home Environment",
"Neighbourhood Environment",
"Parental Diet/Health","Breastfeeding history"),
ordered=T)
RES.bb.use$U <- ifelse(RES.bb.use$`Pr(>|z|)`<0.05,"*","")
ff.plot <- ggplot(subset(RES.bb.use, site=="All"),
aes(x=reorder(Label,Estimate), y=exp(Estimate),alpha=U, fill=Category)) +
theme_bw() +
xlab("Coefficient") +
geom_errorbar(aes(ymin=exp(low_ci),ymax=exp(high_ci)), width=.2, position=position_dodge(0.5)) +
geom_point(position=position_dodge(0.5), size = 3, pch = 21) +
facet_grid( ~ Category,scale="free",space="free_x") +
xlab("") + ylab("Odds ratio on B. infantis presence in the first year of life") +
scale_shape_manual(values=seq(0,10)) +
scale_alpha_discrete(range = c(0.3,1))+
geom_hline(yintercept=1,lty=2)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
scale_fill_manual(values=c( "#8AB17D", "#E9C46A","#F4A261", "#E76F51", "#2A9D8F", "#287271", "#9A4770","black"))
ff.plot
pdf("Figures/Binfantis factors.pdf",height=6, width=12)
ff.plot
dev.off()## quartz_off_screen
## 2SEM model for SES -> Microbiome -> phenotype (Figure 5A)
# rm(list=ls())
# library(phyloseq)
# library(psych)
# library(corrplot)
# library("psych")
# library(ggplot2)
# library(car)
# library(magrittr)
# library(ggcorrplot)
# library(missForest)
# library(xlsx)
# library(WGCNA)
# library(ggpubr)
# library(lavaan)
# library(limma)
# library(metaMint)
# library(lubridate)
# library(Maaslin2)
# library(tidyverse)
# library(phyloseq)
# library(copiome)
# library(ggpubr)
# library(ggforce)
# library(patchwork)
# library(gtsummary)
# library(Maaslin2)
# library(plyr)
# library(ecodist)
# work.dir <- "/Users/darlene.dai/Desktop/SES/Results/manuscript/Code/"
# setwd(work.dir)
# load("/Users/darlene.dai/Desktop/SES/P0 Imputed data for factor analysis.RData")
# load("Results/P0 Clinical data with SES Index.RData")
# load("Results/P1 Processed Metagenomic data.RData")
# load("Results/P3 PCOA data.RData")
# load("Results/P2 Univariate Analysis of SES.Rdata")
# load("Results/P5 Maaslin2 sig ses species mclr.RData")
# source("/Users/darlene.dai/Desktop/SES/Script/P4 SEM update model.R")
# # load("Results/P4 SEM Res microbiome latent phenotype.RData")
# ########################################################################################################
# ses.res <- subset(UVA.res.outcome, Adj.genetic==F & SESVar=="MR1_latent.raw.interquartile" &
# !Variables%in%c("sitetoronto","sitevancouver","sitewinnipeg","male1") &
# Cohort=="all")
#
# outcome.vars <-c("atopy5y", "asthma5y_ep", "overweight", "Tscore_abnormal")
#
#
# sig.use <- sig.sp.ses
# data.meta <- data.frame(sample_data(RBNME_species_MCLR))
#
#
# SampleID.3m<-rownames(subset(data.meta, Visit=="3 month"))
# SampleID.1y<-rownames(subset(data.meta, Visit=="1 year"))
#
# data.sp <- data.frame(phyloseq::otu_table(phyloseq::subset_samples(RBNME_species_MCLR)))
#
# data.sp.3m <- data.sp[SampleID.3m,sig.use]
# data.sp.1y <- data.sp[SampleID.1y,sig.use]
#
# data.sp <- merge(merge(data.meta[,c("SampleID", "SubjectNumber")], data.sp.3m, by.x="SampleID", by.y="row.names"),
# merge(data.meta[,c("SampleID", "SubjectNumber")], data.sp.1y, by.x="SampleID", by.y="row.names"),
# by="SubjectNumber", all=T,suffixes = c(".3m", ".1y"))
#
# for(sig.f in sig.use){
# data.sp$sig.f <- (data.sp[,paste0(sig.f,".3m")] + data.sp[,paste0(sig.f,".1y")])/2
# colnames(data.sp)[colnames(data.sp)=="sig.f"] <- paste0(sig.f,".overall")
# data.sp$sig.f <- data.sp[,paste0(sig.f,".1y")] - data.sp[,paste0(sig.f,".3m")]
# colnames(data.sp)[colnames(data.sp)=="sig.f"] <- paste0(sig.f,".change")
# }
# data.sem <- merge(data.withmissing, data.sp, by="subjectnumber", by.y="SubjectNumber",all.x=T)
# data.sem <- merge(data.sem, merge(data.div[data.div$Visit=="3 month",c("SubjectNumber", "Shannon.x","exact_age","ProcessingPeriod")],
# data.div[data.div$Visit=="1 year",c("SubjectNumber", "Shannon.x","exact_age","ProcessingPeriod")],
# by="SubjectNumber",suffixes = c(".3m",".1y"),all=T),
# by.x="subjectnumber", by.y="SubjectNumber",all.x=T)
# data.sem$exact_age.different <- data.sem$exact_age.1y-data.sem$exact_age.3m
# data.sem$exact_age.mean <- (data.sem$exact_age.1y+data.sem$exact_age.3m)/2
# data.sem$asthma5y_ep <- as.numeric(as.character(data.sem$asthma5y_ep ))
# data.sem$overweight <- as.numeric(as.character(data.sem$overweight ))
# data.sem$Tscore_abnormal <- as.numeric(as.character(data.sem$Tscore_abnormal ))
# data.sem$Shannon.x.change <- data.sem$Shannon.x.1y-data.sem$Shannon.x.3m
# data.sem.use <- data.sem[!is.na(data.sem$MR1_latent.raw),]
#
# ####################################################################################
# sig.p.use <- unique(c("Bifidobacterium_longum.subsp.infantis",sig.use))
# used.l.sp <- c(paste0(sig.p.use , ".3m"),paste0(sig.p.use, ".1y"))
# mm.latent <- c(paste("microbiome =~ ",paste(used.l.sp,collapse="+")))
# resp.check.vars <- c("overweight","asthma5y_ep","Tscore_abnormal","atopy5y")
#
# tech.vars <- c("exact_age.3m","ProcessingPeriod.3m",
# "ProcessingPeriod.1y","exact_age.1y",
# "toronto", "edmonton", "vancouver","male")
#
# m.ses <- paste('microbiome ~', "sesmm*MR1_latent.raw", "+",paste(tech.vars, collapse = " + "))
# m.main <- NULL
# m.test <- NULL
# for(resp.check.var in resp.check.vars){
# cov.check.vars.all <- c("MR1_latent.raw", "microbiome")
# m.main.i <- paste(resp.check.var,"~",
# paste(paste(paste0(resp.check.var,cov.check.vars.all),cov.check.vars.all, sep= "*"),collapse = " + "),
# "+", paste(tech.vars, collapse = " + "))
# m.main <- c(m.main,m.main.i)
#
# m.test.i <- paste(paste0("ind_",resp.check.var)," := ",
# paste(paste0(resp.check.var, "microbiome"),"sesmm",sep="*"))
# m.test.i.tt <- paste(paste0("total_",resp.check.var)," := ",
# paste(paste0(resp.check.var, "microbiome"),"sesmm",sep="*"),
# "+", paste0(resp.check.var,"MR1_latent.raw"))
# m.test <- c(m.test,m.test.i,m.test.i.tt)
# }
# add.m <- paste(paste0(sig.p.use , ".3m"),"~~",paste0(sig.p.use, ".1y"))
# load("Results/SES_M_phenotype_sem_final.RData")
# add.m%in%adj.M.use.final
# data.sem.use$MR1_latent.raw <- data.sem.use$MR1_latent.raw.interquartile
# model.use <-paste(c(mm.latent, m.ses, m.main, m.test, adj.M.use.final),collapse = "\n")
# model.all <- sem(paste(model.use,sep="\n"),
# data=data.sem.use,
# ordered=c(resp.check.vars),
# missing="pairwise")
#
#
# ########################################################################################################################################################################
# model.serial.overweight <- sem(paste(c(c(mm.latent, m.ses, m.main[1], m.test[1:2]),adj.M.use.final),sep="\n"),
# data=data.sem.use,
# ordered=c(resp.check.vars),
# missing="pairwise")
#
# model.serial.asthma <- sem(paste(c(c(mm.latent, m.ses, m.main[2], m.test[3:4]),adj.M.use.final),sep="\n"),
# data=data.sem.use,
# ordered=c(resp.check.vars),
# missing="pairwise")
#
# model.serial.abnor <- sem(paste(c(c(mm.latent, m.ses, m.main[3], m.test[5:6]),adj.M.use.final),sep="\n"),
# data=data.sem.use,
# ordered=c(resp.check.vars),
# missing="pairwise")
#
# model.serial.atopy <- sem(paste(c(c(mm.latent, m.ses, m.main[4], m.test[7:8]),adj.M.use.final),sep="\n"),
# data=data.sem.use,
# ordered=c(resp.check.vars),
# missing="pairwise")
#
# ########################################################################################################################################################################
# model.serial.overweight.lw <- sem(paste(c(c(mm.latent, m.ses, m.main[1], m.test[1:2]),adj.M.use.final),sep="\n"),
# data=data.sem.use,
# ordered=c(resp.check.vars))
#
# model.serial.asthma.lw <- sem(paste(c(c(mm.latent, m.ses, m.main[2], m.test[3:4]),adj.M.use.final),sep="\n"),
# data=data.sem.use,
# ordered=c(resp.check.vars))
#
# model.serial.abnor.lw <- sem(paste(c(c(mm.latent, m.ses, m.main[3], m.test[5:6]),adj.M.use.final),sep="\n"),
# data=data.sem.use,
# ordered=c(resp.check.vars))
#
# model.serial.atopy.lw <- sem(paste(c(c(mm.latent, m.ses, m.main[4], m.test[7:8]),adj.M.use.final),sep="\n"),
# data=data.sem.use,
# ordered=c(resp.check.vars))
#
# save(model.all, adj.M.use.final,
# model.serial.overweight, model.serial.asthma,
# model.serial.abnor, model.serial.atopy,
# model.serial.overweight.lw, model.serial.asthma.lw,
# model.serial.abnor.lw, model.serial.atopy.lw,
# file="Results/SES_M_phenotype_sem_final_seperate.RData")
# summary(model.all,fit=T,standardized=T)
# summary(model.serial.overweight,fit=T,standardized=T)
# summary(model.serial.asthma,fit=T,standardized=T)
# summary(model.serial.abnor,fit=T,standardized=T)
# summary(model.serial.atopy,fit=T,standardized=T)
#
# summary(model.serial.overweight.lw,fit=T,standardized=T)
# summary(model.serial.asthma.lw,fit=T,standardized=T)
# summary(model.serial.abnor.lw,fit=T,standardized=T)
# summary(model.serial.atopy.lw,fit=T,standardized=T)
load("Results/SES_M_phenotype_sem_final_seperate.RData")
summary(model.all,fit=T,standardized=T)## lavaan 0.6-19 ended normally after 652 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 904
##
## Used Total
## Number of observations 1171 2752
## Number of missing patterns 14
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 3265.727 2579.194
## Degrees of freedom 1812 1812
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.920
## Shift parameter 878.728
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 17090.948 8578.000
## Degrees of freedom 2016 2016
## P-value 0.000 0.000
## Scaling correction factor 2.297
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.904 0.883
## Tucker-Lewis Index (TLI) 0.893 0.870
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.026 0.019
## 90 Percent confidence interval - lower 0.025 0.017
## 90 Percent confidence interval - upper 0.028 0.021
## P-value H_0: RMSEA <= 0.050 1.000 1.000
## P-value H_0: RMSEA >= 0.080 0.000 0.000
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.037 0.037
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## microbiome =~
## Bfdbctrm_l...3 1.000 0.206 0.053
## Bct_.SGB1877.3 -0.233 0.152 -1.527 0.127 -0.048 -0.023
## Bct_.SGB1855.3 0.233 0.274 0.852 0.394 0.048 0.014
## Bct_.SGB1861.3 -0.037 0.176 -0.210 0.834 -0.008 -0.003
## Bf_.SGB17278.3 -3.294 1.114 -2.958 0.003 -0.678 -0.267
## Bf_.SGB17234.3 -2.262 0.874 -2.588 0.010 -0.465 -0.143
## Cls_.SGB4037.3 -6.110 2.074 -2.947 0.003 -1.257 -0.459
## C__C5_48.SGB47 -3.445 1.128 -3.055 0.002 -0.709 -0.473
## Eg_.SGB14809.3 -4.584 1.603 -2.860 0.004 -0.943 -0.274
## Ent_.SGB4760.3 4.312 1.433 3.009 0.003 0.887 0.580
## Ent_.SGB7931.3 4.827 1.625 2.970 0.003 0.993 0.497
## Ent_.SGB7962.3 -0.039 0.303 -0.129 0.897 -0.008 -0.002
## Ebc_.SGB4933.3 -1.868 0.604 -3.094 0.002 -0.384 -0.397
## Fc_.SGB15316.3 -1.288 0.461 -2.795 0.005 -0.265 -0.251
## Hng_.SGB4742.3 -3.623 1.270 -2.854 0.004 -0.745 -0.350
## Mdt_.SGB4563.3 -1.220 0.455 -2.682 0.007 -0.251 -0.175
## Mgs_.SGB5868.3 -2.558 0.840 -3.047 0.002 -0.526 -0.462
## Phc_.SGB1815.3 9.928 3.255 3.050 0.002 2.043 0.742
## Phc_.SGB1814.3 -0.186 0.224 -0.832 0.406 -0.038 -0.013
## Rt_.SGB16985.3 0.365 0.193 1.896 0.058 0.075 0.031
## Rmn_.SGB4584.3 -5.481 1.918 -2.857 0.004 -1.128 -0.274
## Rmn_.SGB4608.3 -0.541 0.279 -1.939 0.053 -0.111 -0.084
## Rt_.SGB15271.3 1.327 0.501 2.650 0.008 0.273 0.140
## Str_.SGB8065.3 0.058 0.253 0.229 0.819 0.012 0.003
## Str_.SGB8168.3 1.329 0.535 2.485 0.013 0.273 0.129
## Str_.SGB8084.3 0.278 0.169 1.643 0.100 0.057 0.027
## Str_.SGB8005.3 0.203 0.250 0.813 0.416 0.042 0.012
## St_.SGB8007_.3 -0.342 0.272 -1.260 0.208 -0.070 -0.022
## Str_.SGB8002.3 -2.525 0.890 -2.836 0.005 -0.519 -0.220
## Vll_.SGB6936.3 -0.087 0.152 -0.569 0.569 -0.018 -0.008
## Bfdbctrm_l...1 1.763 0.579 3.042 0.002 0.363 0.095
## Bct_.SGB1877.1 -0.294 0.225 -1.310 0.190 -0.061 -0.022
## Bct_.SGB1855.1 0.401 0.356 1.125 0.261 0.083 0.022
## Bct_.SGB1861.1 -0.801 0.375 -2.136 0.033 -0.165 -0.057
## Bf_.SGB17278.1 -0.015 0.310 -0.049 0.961 -0.003 -0.001
## Bf_.SGB17234.1 0.745 0.333 2.240 0.025 0.153 0.049
## Cls_.SGB4037.1 -0.024 0.334 -0.072 0.943 -0.005 -0.001
## C__C5_48.SGB47 -0.173 0.235 -0.735 0.463 -0.036 -0.011
## Eg_.SGB14809.1 0.197 0.265 0.745 0.456 0.041 0.013
## Ent_.SGB4760.1 4.588 1.560 2.941 0.003 0.944 0.422
## Ent_.SGB7931.1 0.576 0.269 2.138 0.033 0.119 0.046
## Ent_.SGB7962.1 4.199 1.386 3.030 0.002 0.864 0.425
## Ebc_.SGB4933.1 -0.426 0.372 -1.145 0.252 -0.088 -0.024
## Fc_.SGB15316.1 -0.476 0.379 -1.256 0.209 -0.098 -0.023
## Hng_.SGB4742.1 -1.070 0.459 -2.330 0.020 -0.220 -0.076
## Mdt_.SGB4563.1 -0.251 0.333 -0.754 0.451 -0.052 -0.015
## Mgs_.SGB5868.1 1.759 0.677 2.600 0.009 0.362 0.140
## Phc_.SGB1815.1 -0.216 0.270 -0.801 0.423 -0.044 -0.014
## Phc_.SGB1814.1 -0.572 0.385 -1.486 0.137 -0.118 -0.031
## Rt_.SGB16985.1 0.051 0.055 0.929 0.353 0.011 0.013
## Rmn_.SGB4584.1 -15.197 4.963 -3.062 0.002 -3.127 -1.005
## Rmn_.SGB4608.1 -1.884 0.780 -2.415 0.016 -0.388 -0.131
## Rt_.SGB15271.1 -0.921 0.376 -2.453 0.014 -0.190 -0.064
## Str_.SGB8065.1 0.191 0.150 1.273 0.203 0.039 0.020
## Str_.SGB8168.1 1.516 0.517 2.931 0.003 0.312 0.532
## Str_.SGB8084.1 1.031 0.384 2.687 0.007 0.212 0.263
## Str_.SGB8005.1 1.228 0.611 2.008 0.045 0.253 0.080
## St_.SGB8007_.1 -0.033 0.301 -0.109 0.913 -0.007 -0.002
## Str_.SGB8002.1 -0.655 0.399 -1.642 0.101 -0.135 -0.033
## Vll_.SGB6936.1 1.317 0.458 2.874 0.004 0.271 0.092
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## microbiome ~
## MR1_lt. (ssmm) 0.021 0.009 2.495 0.013 0.104 0.074
## exct_.3 -0.379 0.132 -2.873 0.004 -1.842 -0.170
## PrcsP.3 -0.000 0.000 -0.163 0.870 -0.000 -0.003
## PrcsP.1 -0.000 0.000 -0.419 0.676 -0.001 -0.007
## exct_.1 -0.018 0.034 -0.522 0.602 -0.087 -0.009
## toronto 0.017 0.013 1.341 0.180 0.083 0.037
## edmontn 0.005 0.011 0.458 0.647 0.025 0.010
## vancovr 0.033 0.015 2.276 0.023 0.161 0.070
## male -0.014 0.008 -1.659 0.097 -0.068 -0.034
## overweight ~
## MR1_lt. (oMR1) -0.063 0.070 -0.904 0.366 -0.063 -0.044
## microbm (ovrw) -0.688 0.226 -3.042 0.002 -0.142 -0.139
## exct_.3 -0.942 0.572 -1.646 0.100 -0.942 -0.085
## PrcsP.3 0.001 0.003 0.259 0.796 0.001 0.011
## PrcsP.1 0.000 0.003 0.080 0.936 0.000 0.003
## exct_.1 -0.794 0.492 -1.614 0.106 -0.794 -0.082
## toronto -0.213 0.144 -1.481 0.139 -0.213 -0.092
## edmontn 0.002 0.134 0.017 0.986 0.002 0.001
## vancovr -0.142 0.122 -1.164 0.245 -0.142 -0.061
## male 0.085 0.090 0.946 0.344 0.085 0.042
## asthma5y_ep ~
## MR1_lt. (a5_M) -0.116 0.090 -1.300 0.194 -0.116 -0.081
## microbm (as5_) -0.788 0.310 -2.541 0.011 -0.162 -0.159
## exct_.3 -1.353 0.850 -1.591 0.112 -1.353 -0.123
## PrcsP.3 0.005 0.003 1.369 0.171 0.005 0.076
## PrcsP.1 -0.004 0.005 -0.899 0.368 -0.004 -0.055
## exct_.1 0.028 0.572 0.048 0.961 0.028 0.003
## toronto 0.142 0.192 0.739 0.460 0.142 0.062
## edmontn 0.119 0.203 0.584 0.559 0.119 0.044
## vancovr 0.246 0.162 1.524 0.128 0.246 0.105
## male 0.174 0.126 1.373 0.170 0.174 0.085
## Tscore_abnormal ~
## MR1_lt. (T_MR) -0.202 0.111 -1.819 0.069 -0.202 -0.137
## microbm (Tsc_) -4.795 1.521 -3.153 0.002 -0.987 -0.947
## exct_.3 -3.051 0.819 -3.724 0.000 -3.051 -0.271
## PrcsP.3 0.003 0.004 0.647 0.518 0.003 0.040
## PrcsP.1 -0.001 0.005 -0.194 0.846 -0.001 -0.011
## exct_.1 1.159 0.690 1.679 0.093 1.159 0.117
## toronto 0.216 0.249 0.868 0.386 0.216 0.092
## edmontn -0.101 0.222 -0.453 0.651 -0.101 -0.037
## vancovr 0.357 0.202 1.771 0.077 0.357 0.149
## male 0.089 0.148 0.601 0.548 0.089 0.043
## atopy5y ~
## MR1_lt. (a5MR) -0.061 0.067 -0.901 0.368 -0.061 -0.041
## microbm (atp5) 0.138 0.108 1.282 0.200 0.028 0.027
## exct_.3 -0.749 0.524 -1.429 0.153 -0.749 -0.066
## PrcsP.3 -0.003 0.003 -1.022 0.307 -0.003 -0.042
## PrcsP.1 -0.005 0.003 -1.585 0.113 -0.005 -0.069
## exct_.1 -0.595 0.449 -1.325 0.185 -0.595 -0.060
## toronto 0.690 0.138 4.990 0.000 0.690 0.290
## edmontn 0.512 0.141 3.638 0.000 0.512 0.185
## vancovr 0.553 0.123 4.477 0.000 0.553 0.228
## male 0.281 0.089 3.154 0.002 0.281 0.133
##
## Covariances:
## Estimate Std.Err z-value
## .Clostridium_innocuum.SGB4037.3m ~~
## .Cls_.SGB4037.1 0.690 0.299 2.309
## .Clostridium_sp_C5_48.SGB4752.3m ~~
## .C__C5_48.SGB47 0.964 0.169 5.709
## .Enterococcus_avium.SGB7931.3m ~~
## .Ent_.SGB7931.1 0.545 0.136 3.998
## .Enterococcus_faecalis.SGB7962.3m ~~
## .Ent_.SGB7962.1 0.384 0.215 1.782
## .Megasphaera_micronuciformis.SGB5868.3m ~~
## .Mgs_.SGB5868.1 0.210 0.080 2.626
## .Rothia_mucilaginosa.SGB16985.3m ~~
## .Rt_.SGB16985.1 -0.058 0.048 -1.210
## .Ruminococcus_gnavus.SGB4584.3m ~~
## .Rmn_.SGB4584.1 -1.782 0.606 -2.941
## .Ruminococcus_torques.SGB4608.3m ~~
## .Rmn_.SGB4608.1 0.604 0.091 6.623
## .Streptococcus_lactarius.SGB8065.3m ~~
## .Str_.SGB8065.1 0.083 0.188 0.440
## .Streptococcus_mitis.SGB8168.3m ~~
## .Str_.SGB8168.1 -0.003 0.034 -0.084
## .Streptococcus_peroris.SGB8084.3m ~~
## .Str_.SGB8084.1 0.059 0.040 1.499
## .Streptococcus_salivarius.SGB8005.3m ~~
## .Str_.SGB8005.1 0.973 0.320 3.038
## .Streptococcus_salivarius.SGB8007_group.3m ~~
## .St_.SGB8007_.1 0.265 0.315 0.842
## .Streptococcus_thermophilus.SGB8002.3m ~~
## .Str_.SGB8002.1 0.476 0.284 1.676
## .Ruminococcus_gnavus.SGB4584.1y ~~
## .Str_.SGB8084.1 0.660 0.132 4.983
## .Rothia_mucilaginosa.SGB16985.1y ~~
## .Str_.SGB8084.1 0.279 0.020 14.245
## .Streptococcus_mitis.SGB8168.1y ~~
## .Str_.SGB8084.1 0.064 0.017 3.797
## .Streptococcus_lactarius.SGB8065.1y ~~
## .Str_.SGB8084.1 0.574 0.049 11.611
## .Rothia_mucilaginosa.SGB16985.1y ~~
## .Str_.SGB8065.1 0.649 0.065 9.907
## .Str_.SGB8168.1 0.050 0.006 7.981
## .Eubacterium_rectale.SGB4933.3m ~~
## .Rt_.SGB15271.3 0.438 0.047 9.373
## .Rmn_.SGB4608.3 0.139 0.022 6.374
## .Streptococcus_lactarius.SGB8065.1y ~~
## .Str_.SGB8005.1 2.911 0.341 8.526
## .Ruminococcus_torques.SGB4608.3m ~~
## .Rt_.SGB15271.3 0.567 0.064 8.880
## .Phocaeicola_dorei.SGB1815.3m ~~
## .Phc_.SGB1815.1 4.678 0.571 8.197
## .Streptococcus_mitis.SGB8168.3m ~~
## .Str_.SGB8084.3 1.820 0.222 8.195
## .Eubacterium_rectale.SGB4933.3m ~~
## .Str_.SGB8002.3 0.140 0.033 4.231
## .Str_.SGB8168.1 0.155 0.020 7.651
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Ent_.SGB4760.1 -0.102 0.119 -0.856
## .Bacteroides_caccae.SGB1877.3m ~~
## .Bct_.SGB1877.1 2.373 0.298 7.972
## .Clostridium_innocuum.SGB4037.1y ~~
## .Rmn_.SGB4584.1 4.393 0.516 8.521
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Str_.SGB8002.3 1.390 0.166 8.384
## .Bacteroides_thetaiotaomicron.SGB1861.3m ~~
## .Bct_.SGB1861.1 2.979 0.386 7.723
## .Clostridium_innocuum.SGB4037.3m ~~
## .Rt_.SGB15271.3 1.821 0.239 7.611
## .Eubacterium_rectale.SGB4933.3m ~~
## .Mdt_.SGB4563.3 0.047 0.018 2.591
## .Fc_.SGB15316.3 -0.013 0.020 -0.674
## .Eggerthella_lenta.SGB14809.1y ~~
## .Rmn_.SGB4584.1 2.859 0.357 8.017
## .Clostridium_innocuum.SGB4037.1y ~~
## .Eg_.SGB14809.1 3.722 0.494 7.539
## .Clostridium_innocuum.SGB4037.3m ~~
## .Rmn_.SGB4608.3 0.654 0.096 6.781
## .Bifidobacterium_longum.subsp.infantis.3m ~~
## .Bfdbctrm_l...1 5.888 0.789 7.466
## .Bacteroides_caccae.SGB1877.3m ~~
## .Phc_.SGB1814.3 1.564 0.216 7.256
## .Hungatella_hathewayi.SGB4742.3m ~~
## .Str_.SGB8168.1 0.327 0.046 7.121
## .Clostridium_sp_C5_48.SGB4752.3m ~~
## .Mgs_.SGB5868.3 -0.161 0.045 -3.573
## .Phocaeicola_vulgatus.SGB1814.3m ~~
## .Phc_.SGB1814.1 4.961 0.699 7.101
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Str_.SGB8002.3 0.244 0.052 4.660
## .Bacteroides_caccae.SGB1877.3m ~~
## .Phc_.SGB1815.3 1.210 0.172 7.023
## .Rothia_mucilaginosa.SGB16985.3m ~~
## .Str_.SGB8084.3 1.498 0.212 7.080
## .Rothia_mucilaginosa.SGB16985.1y ~~
## .Str_.SGB8005.1 0.736 0.111 6.635
## .Bifidobacterium_animalis.SGB17278.1y ~~
## .Str_.SGB8002.1 6.153 0.936 6.570
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Rt_.SGB15271.3 0.871 0.136 6.405
## .Bacteroides_caccae.SGB1877.3m ~~
## .Bct_.SGB1861.3 0.886 0.138 6.406
## .Streptococcus_lactarius.SGB8065.3m ~~
## .St_.SGB8007_.3 -3.831 0.594 -6.450
## .Eubacterium_rectale.SGB4933.3m ~~
## .Phc_.SGB1815.3 0.913 0.110 8.268
## .Bacteroides_thetaiotaomicron.SGB1861.3m ~~
## .Phc_.SGB1815.3 1.193 0.205 5.819
## .Bacteroides_fragilis.SGB1855.3m ~~
## .Bct_.SGB1855.1 5.945 0.960 6.196
## .Mediterraneibacter_faecis.SGB4563.3m ~~
## .Mdt_.SGB4563.1 0.829 0.139 5.975
## .Eggerthella_lenta.SGB14809.1y ~~
## .Hng_.SGB4742.1 2.043 0.329 6.201
## .Streptococcus_mitis.SGB8168.3m ~~
## .St_.SGB8007_.3 1.399 0.227 6.168
## .Enterocloster_clostridioformis.SGB4760.1y ~~
## .Hng_.SGB4742.1 1.667 0.274 6.074
## .Rmn_.SGB4584.1 4.060 0.513 7.907
## .Streptococcus_salivarius.SGB8007_group.3m ~~
## .Str_.SGB8002.3 1.522 0.247 6.173
## .Bifidobacterium_dentium.SGB17234.1y ~~
## .Rt_.SGB16985.1 0.356 0.060 5.970
## .Rothia_mucilaginosa.SGB16985.3m ~~
## .Str_.SGB8005.3 2.090 0.344 6.068
## .Clostridium_innocuum.SGB4037.3m ~~
## .Str_.SGB8002.3 0.597 0.165 3.616
## .Enterococcus_faecalis.SGB7962.1y ~~
## .Str_.SGB8065.1 0.536 0.094 5.691
## .Streptococcus_lactarius.SGB8065.1y ~~
## .Str_.SGB8168.1 0.106 0.021 4.983
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Rt_.SGB15271.3 0.082 0.064 1.279
## .Eggerthella_lenta.SGB14809.1y ~~
## .Rt_.SGB15271.1 2.386 0.410 5.826
## .Bifidobacterium_dentium.SGB17234.1y ~~
## .Vll_.SGB6936.1 1.840 0.321 5.735
## .Streptococcus_salivarius.SGB8007_group.1y ~~
## .Vll_.SGB6936.1 2.089 0.367 5.689
## .Streptococcus_salivarius.SGB8005.3m ~~
## .St_.SGB8007_.3 -4.017 0.697 -5.765
## .Eubacterium_rectale.SGB4933.3m ~~
## .Ent_.SGB7962.1 0.431 0.053 8.195
## .Ruminococcus_torques.SGB4608.1y ~~
## .Rt_.SGB15271.1 2.234 0.395 5.655
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Rmn_.SGB4608.3 0.075 0.016 4.773
## .Streptococcus_salivarius.SGB8005.1y ~~
## .St_.SGB8007_.1 -2.130 0.398 -5.358
## .Bacteroides_caccae.SGB1877.1y ~~
## .Phc_.SGB1814.1 2.907 0.550 5.287
## .Eggerthella_lenta.SGB14809.3m ~~
## .Eg_.SGB14809.1 2.264 0.429 5.279
## .Enterococcus_avium.SGB7931.3m ~~
## .Str_.SGB8002.3 1.004 0.152 6.598
## .Rmn_.SGB4608.3 0.333 0.069 4.825
## .Clostridium_innocuum.SGB4037.1y ~~
## .Ent_.SGB4760.1 1.571 0.314 4.995
## .Ruthenibacterium_lactatiformans.SGB15271.3m ~~
## .Rt_.SGB15271.1 1.066 0.206 5.169
## .Eggerthella_lenta.SGB14809.3m ~~
## .Rt_.SGB15271.3 1.660 0.310 5.352
## .Mediterraneibacter_faecis.SGB4563.1y ~~
## .Rmn_.SGB4584.1 -1.934 0.393 -4.926
## .Mediterraneibacter_faecis.SGB4563.3m ~~
## .Ent_.SGB7962.1 0.403 0.065 6.158
## .Bifidobacterium_dentium.SGB17234.3m ~~
## .Bf_.SGB17234.1 1.588 0.302 5.255
## .Clostridium_innocuum.SGB4037.1y ~~
## .Rt_.SGB15271.1 2.515 0.484 5.202
## .St_.SGB8007_.1 2.213 0.429 5.157
## .Rothia_mucilaginosa.SGB16985.1y ~~
## .Vll_.SGB6936.1 0.289 0.057 5.039
## .Streptococcus_salivarius.SGB8005.1y ~~
## .Vll_.SGB6936.1 1.643 0.317 5.177
## .Eubacterium_rectale.SGB4933.1y ~~
## .Mdt_.SGB4563.1 2.795 0.556 5.023
## .Streptococcus_lactarius.SGB8065.3m ~~
## .Str_.SGB8005.3 2.602 0.515 5.051
## .Enterococcus_avium.SGB7931.3m ~~
## .Ebc_.SGB4933.3 0.474 0.060 7.864
## .Bacteroides_thetaiotaomicron.SGB1861.1y ~~
## .Phc_.SGB1815.1 1.849 0.373 4.962
## .Bacteroides_thetaiotaomicron.SGB1861.3m ~~
## .Mgs_.SGB5868.3 0.265 0.055 4.794
## .Megasphaera_micronuciformis.SGB5868.1y ~~
## .Str_.SGB8005.1 1.390 0.278 4.996
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Str_.SGB8168.1 -0.223 0.047 -4.712
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Str_.SGB8168.1 0.112 0.021 5.445
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Bf_.SGB17234.3 0.863 0.190 4.539
## .Eggerthella_lenta.SGB14809.1y ~~
## .Ent_.SGB7931.1 1.313 0.269 4.887
## .Hungatella_hathewayi.SGB4742.3m ~~
## .Rt_.SGB15271.3 0.588 0.111 5.296
## .Bacteroides_thetaiotaomicron.SGB1861.3m ~~
## .Phc_.SGB1814.3 0.984 0.199 4.935
## .Ruminococcus_gnavus.SGB4584.3m ~~
## .Rt_.SGB15271.3 1.372 0.282 4.858
## .Eubacterium_rectale.SGB4933.3m ~~
## .Ebc_.SGB4933.1 0.396 0.091 4.375
## .Bacteroides_caccae.SGB1877.1y ~~
## .Bct_.SGB1861.1 1.322 0.274 4.818
## .Clostridium_innocuum.SGB4037.3m ~~
## .Ent_.SGB4760.3 1.691 0.209 8.072
## .Bifidobacterium_animalis.SGB17278.1y ~~
## .Cls_.SGB4037.1 2.599 0.530 4.901
## .Clostridium_innocuum.SGB4037.3m ~~
## .C__C5_48.SGB47 -0.404 0.118 -3.418
## .Bifidobacterium_longum.subsp.infantis.3m ~~
## .Str_.SGB8065.3 2.355 0.487 4.837
## .Streptococcus_lactarius.SGB8065.3m ~~
## .Str_.SGB8168.3 1.106 0.227 4.870
## .Streptococcus_mitis.SGB8168.3m ~~
## .Vll_.SGB6936.3 0.558 0.124 4.505
## .Eubacterium_rectale.SGB4933.1y ~~
## .Rmn_.SGB4608.1 1.883 0.408 4.620
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Vll_.SGB6936.3 0.620 0.120 5.145
## .Eggerthella_lenta.SGB14809.3m ~~
## .Rmn_.SGB4584.3 2.063 0.485 4.250
## .Ruminococcus_gnavus.SGB4584.1y ~~
## .Str_.SGB8002.1 1.793 0.450 3.985
## .Clostridium_sp_C5_48.SGB4752.1y ~~
## .Rmn_.SGB4584.1 1.757 0.400 4.392
## .Bifidobacterium_longum.subsp.infantis.1y ~~
## .Vll_.SGB6936.1 1.577 0.348 4.534
## .Hungatella_hathewayi.SGB4742.3m ~~
## .Mgs_.SGB5868.3 -0.137 0.063 -2.178
## .Streptococcus_lactarius.SGB8065.3m ~~
## .Bfdbctrm_l...1 2.155 0.459 4.694
## .Bacteroides_thetaiotaomicron.SGB1861.1y ~~
## .Phc_.SGB1814.1 2.080 0.452 4.596
## .Streptococcus_salivarius.SGB8007_group.1y ~~
## .Str_.SGB8002.1 2.244 0.491 4.568
## .Megasphaera_micronuciformis.SGB5868.1y ~~
## .Str_.SGB8065.1 0.598 0.132 4.546
## .Bacteroides_caccae.SGB1877.3m ~~
## .Phc_.SGB1814.1 1.190 0.263 4.519
## .Clostridium_innocuum.SGB4037.1y ~~
## .C__C5_48.SGB47 2.143 0.471 4.545
## .Rothia_mucilaginosa.SGB16985.3m ~~
## .St_.SGB8007_.3 -1.392 0.306 -4.542
## .Enterococcus_avium.SGB7931.1y ~~
## .Str_.SGB8005.1 1.123 0.247 4.549
## .Megasphaera_micronuciformis.SGB5868.1y ~~
## .Rt_.SGB16985.1 0.212 0.047 4.490
## .Mediterraneibacter_faecis.SGB4563.3m ~~
## .Rt_.SGB15271.3 0.263 0.057 4.620
## .Eggerthella_lenta.SGB14809.3m ~~
## .Ent_.SGB7931.3 1.781 0.323 5.519
## .Enterococcus_avium.SGB7931.3m ~~
## .Fc_.SGB15316.3 0.371 0.059 6.261
## .Bacteroides_fragilis.SGB1855.3m ~~
## .Bct_.SGB1861.3 1.094 0.257 4.264
## .Clostridium_sp_C5_48.SGB4752.3m ~~
## .Phc_.SGB1814.3 0.384 0.089 4.293
## .Clostridium_innocuum.SGB4037.1y ~~
## .Ent_.SGB7931.1 1.266 0.300 4.215
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Rt_.SGB15271.3 0.210 0.044 4.785
## .Enterococcus_avium.SGB7931.1y ~~
## .Rmn_.SGB4584.1 1.079 0.277 3.900
## .Enterococcus_avium.SGB7931.3m ~~
## .Rmn_.SGB4584.3 1.800 0.338 5.328
## .Ruminococcus_gnavus.SGB4584.3m ~~
## .Str_.SGB8168.1 0.508 0.084 6.026
## .Phocaeicola_vulgatus.SGB1814.3m ~~
## .Bct_.SGB1877.1 1.009 0.236 4.271
## .Eggerthella_lenta.SGB14809.1y ~~
## .Rmn_.SGB4608.1 1.519 0.357 4.250
## .Eggerthella_lenta.SGB14809.3m ~~
## .Str_.SGB8002.3 0.735 0.224 3.283
## .Hungatella_hathewayi.SGB4742.3m ~~
## .Hng_.SGB4742.1 0.600 0.161 3.732
## .Eggerthella_lenta.SGB14809.3m ~~
## .Rmn_.SGB4608.3 0.473 0.120 3.943
## .Hungatella_hathewayi.SGB4742.1y ~~
## .Rt_.SGB15271.1 1.170 0.285 4.099
## .Streptococcus_mitis.SGB8168.1y ~~
## .St_.SGB8007_.1 0.184 0.048 3.807
## .Bifidobacterium_longum.subsp.infantis.1y ~~
## .Ent_.SGB7931.1 1.081 0.256 4.229
## .Bifidobacterium_dentium.SGB17234.1y ~~
## .Str_.SGB8005.1 1.204 0.303 3.979
## .Eubacterium_rectale.SGB4933.3m ~~
## .Hng_.SGB4742.3 -0.156 0.040 -3.876
## .Streptococcus_peroris.SGB8084.3m ~~
## .Str_.SGB8005.3 0.823 0.204 4.031
## .Enterococcus_avium.SGB7931.1y ~~
## .Vll_.SGB6936.1 0.882 0.223 3.951
## .Ruminococcus_gnavus.SGB4584.3m ~~
## .Rmn_.SGB4608.3 0.563 0.143 3.928
## .Mediterraneibacter_faecis.SGB4563.1y ~~
## .Rmn_.SGB4608.1 1.229 0.318 3.865
## .Bacteroides_caccae.SGB1877.1y ~~
## .Phc_.SGB1815.1 1.045 0.269 3.890
## .Clostridium_innocuum.SGB4037.3m ~~
## .Rmn_.SGB4608.1 0.470 0.197 2.386
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Mgs_.SGB5868.3 -0.098 0.059 -1.672
## .Clostridium_innocuum.SGB4037.3m ~~
## .Ebc_.SGB4933.3 -0.239 0.068 -3.525
## .Bifidobacterium_dentium.SGB17234.3m ~~
## .Mgs_.SGB5868.3 0.072 0.074 0.971
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Rmn_.SGB4584.3 1.768 0.286 6.190
## .Eubacterium_rectale.SGB4933.3m ~~
## .Rmn_.SGB4608.1 0.110 0.061 1.812
## .Bifidobacterium_longum.subsp.infantis.3m ~~
## .Str_.SGB8084.3 0.839 0.220 3.807
## .Bifidobacterium_dentium.SGB17234.1y ~~
## .Hng_.SGB4742.1 1.082 0.276 3.918
## .Phocaeicola_dorei.SGB1815.3m ~~
## .Bct_.SGB1861.1 1.154 0.263 4.390
## .Ruthenibacterium_lactatiformans.SGB15271.3m ~~
## .Str_.SGB8002.3 0.538 0.126 4.271
## .Streptococcus_thermophilus.SGB8002.3m ~~
## .Ent_.SGB4760.1 0.911 0.163 5.601
## .Rothia_mucilaginosa.SGB16985.3m ~~
## .Vll_.SGB6936.3 0.499 0.133 3.764
## .Streptococcus_lactarius.SGB8065.1y ~~
## .St_.SGB8007_.1 -0.685 0.190 -3.600
## .Ruthenibacterium_lactatiformans.SGB15271.1y ~~
## .Vll_.SGB6936.1 -1.061 0.303 -3.500
## .Eggerthella_lenta.SGB14809.1y ~~
## .St_.SGB8007_.1 1.139 0.315 3.622
## .Ruthenibacterium_lactatiformans.SGB15271.3m ~~
## .Rmn_.SGB4608.1 0.601 0.159 3.795
## .Eggerthella_lenta.SGB14809.1y ~~
## .Str_.SGB8002.1 1.464 0.402 3.641
## .Eubacterium_rectale.SGB4933.1y ~~
## .Fc_.SGB15316.1 2.248 0.626 3.591
## .Phocaeicola_dorei.SGB1815.1y ~~
## .Phc_.SGB1814.1 -1.661 0.469 -3.539
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Rmn_.SGB4584.3 0.082 0.092 0.897
## .Mediterraneibacter_faecis.SGB4563.3m ~~
## .Rmn_.SGB4608.3 0.074 0.027 2.801
## .Bifidobacterium_dentium.SGB17234.3m ~~
## .Ebc_.SGB4933.3 -0.009 0.048 -0.183
## .Bifidobacterium_longum.subsp.infantis.1y ~~
## .Str_.SGB8005.1 1.220 0.351 3.478
## .Bifidobacterium_dentium.SGB17234.1y ~~
## .Ent_.SGB7931.1 0.796 0.227 3.509
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Mdt_.SGB4563.3 0.098 0.065 1.503
## .Streptococcus_lactarius.SGB8065.1y ~~
## .Vll_.SGB6936.1 0.535 0.157 3.409
## .Bacteroides_fragilis.SGB1855.1y ~~
## .Eg_.SGB14809.1 1.362 0.398 3.423
## .Streptococcus_thermophilus.SGB8002.3m ~~
## .Rmn_.SGB4608.1 0.423 0.170 2.493
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Vll_.SGB6936.3 0.119 0.038 3.161
## .Clostridium_sp_C5_48.SGB4752.3m ~~
## .Str_.SGB8005.3 0.397 0.119 3.330
## .Bacteroides_thetaiotaomicron.SGB1861.3m ~~
## .Fc_.SGB15316.3 0.133 0.043 3.060
## .Faecalibacterium_prausnitzii.SGB15316.1y ~~
## .Mdt_.SGB4563.1 1.498 0.486 3.081
## .Phocaeicola_vulgatus.SGB1814.3m ~~
## .Str_.SGB8084.1 0.169 0.052 3.275
## .Phocaeicola_dorei.SGB1815.3m ~~
## .Rmn_.SGB4584.3 3.102 0.531 5.846
## .Clostridium_innocuum.SGB4037.3m ~~
## .Ent_.SGB7931.3 1.537 0.246 6.260
## .Enterococcus_avium.SGB7931.3m ~~
## .Str_.SGB8084.1 -0.235 0.068 -3.453
## .Rmn_.SGB4584.1 2.899 0.406 7.133
## .Phocaeicola_dorei.SGB1815.3m ~~
## .Str_.SGB8084.1 -0.423 0.105 -4.031
## .Rmn_.SGB4584.1 6.174 0.825 7.485
## .Eggerthella_lenta.SGB14809.3m ~~
## .Str_.SGB8084.1 0.287 0.083 3.445
## .Ruminococcus_gnavus.SGB4584.1y ~~
## .Str_.SGB8168.1 0.968 0.163 5.954
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Str_.SGB8084.1 -0.191 0.051 -3.722
## .Fc_.SGB15316.3 0.153 0.054 2.845
## .Enterocloster_clostridioformis.SGB4760.1y ~~
## .Str_.SGB8084.1 -0.166 0.057 -2.887
## .Ruminococcus_gnavus.SGB4584.3m ~~
## .Ent_.SGB4760.1 1.677 0.364 4.605
## .Str_.SGB8084.1 0.184 0.107 1.715
## .Ruminococcus_gnavus.SGB4584.1y ~~
## .Vll_.SGB6936.1 1.491 0.336 4.444
## .Hungatella_hathewayi.SGB4742.3m ~~
## .Str_.SGB8084.1 0.150 0.051 2.925
## .Enterococcus_avium.SGB7931.3m ~~
## .Hng_.SGB4742.3 0.910 0.154 5.897
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Ent_.SGB7931.3 0.854 0.152 5.618
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Rmn_.SGB4584.1 2.808 0.372 7.553
## .Hungatella_hathewayi.SGB4742.1y ~~
## .Str_.SGB8084.1 0.113 0.060 1.894
## .Streptococcus_thermophilus.SGB8002.3m ~~
## .Str_.SGB8084.1 0.071 0.045 1.557
## .Ent_.SGB7962.1 0.664 0.127 5.236
## .Megasphaera_micronuciformis.SGB5868.3m ~~
## .Phc_.SGB1815.3 1.175 0.144 8.145
## .Eggerthella_lenta.SGB14809.3m ~~
## .Phc_.SGB1815.3 2.478 0.472 5.254
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Ent_.SGB7931.3 -0.834 0.145 -5.766
## .Enterococcus_faecalis.SGB7962.1y ~~
## .Str_.SGB8084.1 -0.081 0.045 -1.788
## .Mediterraneibacter_faecis.SGB4563.3m ~~
## .Rmn_.SGB4584.1 -0.782 0.191 -4.100
## .Phocaeicola_vulgatus.SGB1814.3m ~~
## .Rt_.SGB15271.3 0.444 0.144 3.095
## .Clostridium_innocuum.SGB4037.3m ~~
## .Str_.SGB8084.1 0.202 0.081 2.482
## .Ent_.SGB7962.1 1.324 0.220 6.012
## .Bifidobacterium_dentium.SGB17234.3m ~~
## .Str_.SGB8084.1 0.128 0.057 2.251
## .Enterococcus_faecalis.SGB7962.1y ~~
## .Rmn_.SGB4584.1 2.254 0.362 6.217
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Ent_.SGB7962.1 -0.707 0.121 -5.852
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Ent_.SGB7962.1 0.763 0.144 5.290
## .Eggerthella_lenta.SGB14809.3m ~~
## .Rmn_.SGB4584.1 -2.458 0.537 -4.577
## .Ent_.SGB7962.1 1.108 0.262 4.231
## .Ent_.SGB4760.1 1.319 0.304 4.341
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Rmn_.SGB4584.1 -1.670 0.295 -5.666
## .Str_.SGB8084.1 0.071 0.082 0.866
## .Str_.SGB8168.1 0.253 0.042 5.978
## .Clostridium_innocuum.SGB4037.3m ~~
## .Rmn_.SGB4584.1 -3.187 0.526 -6.055
## .Ruminococcus_gnavus.SGB4584.1y ~~
## .Str_.SGB8005.1 0.813 0.375 2.170
## .Hungatella_hathewayi.SGB4742.3m ~~
## .Rmn_.SGB4584.1 -1.885 0.361 -5.222
## .Eubacterium_rectale.SGB4933.3m ~~
## .Rmn_.SGB4584.1 -1.124 0.149 -7.571
## .Str_.SGB8084.1 0.067 0.026 2.622
## .Bifidobacterium_longum.subsp.infantis.1y ~~
## .Str_.SGB8084.1 0.001 0.065 0.014
## .Clostridium_innocuum.SGB4037.1y ~~
## .Str_.SGB8084.1 0.014 0.061 0.237
## .Ruthenibacterium_lactatiformans.SGB15271.3m ~~
## .Rmn_.SGB4584.1 1.110 0.284 3.910
## .Str_.SGB8084.1 -0.096 0.065 -1.482
## .Rothia_mucilaginosa.SGB16985.3m ~~
## .Str_.SGB8084.1 -0.012 0.045 -0.268
## .Streptococcus_thermophilus.SGB8002.3m ~~
## .Rmn_.SGB4584.1 -0.887 0.328 -2.703
## .Bacteroides_thetaiotaomicron.SGB1861.1y ~~
## .Str_.SGB8084.1 0.109 0.054 2.020
## .Megasphaera_micronuciformis.SGB5868.1y ~~
## .Rmn_.SGB4584.1 1.527 0.362 4.218
## .Ruminococcus_gnavus.SGB4584.3m ~~
## .Ent_.SGB7962.1 0.996 0.286 3.478
## .Enterococcus_faecalis.SGB7962.1y ~~
## .Rmn_.SGB4608.1 0.619 0.182 3.392
## .Bacteroides_thetaiotaomicron.SGB1861.3m ~~
## .Str_.SGB8084.1 0.112 0.037 2.995
## .Hungatella_hathewayi.SGB4742.1y ~~
## .Vll_.SGB6936.1 0.743 0.248 2.996
## .Eubacterium_rectale.SGB4933.3m ~~
## .Bct_.SGB1861.1 -0.186 0.055 -3.358
## .Faecalibacterium_prausnitzii.SGB15316.1y ~~
## .Rmn_.SGB4584.1 -1.603 0.489 -3.275
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Str_.SGB8084.1 0.040 0.025 1.603
## .Rmn_.SGB4584.1 -0.756 0.173 -4.379
## .Ruminococcus_torques.SGB4608.3m ~~
## .Str_.SGB8084.1 0.007 0.039 0.189
## .Rmn_.SGB4584.1 -0.197 0.145 -1.365
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Fc_.SGB15316.3 -0.242 0.077 -3.167
## .Ruthenibacterium_lactatiformans.SGB15271.3m ~~
## .Ebc_.SGB4933.1 0.655 0.208 3.148
## .Clostridium_sp_C5_48.SGB4752.3m ~~
## .Ent_.SGB7931.3 0.739 0.111 6.645
## .Phocaeicola_dorei.SGB1815.3m ~~
## .St_.SGB8007_.3 0.818 0.280 2.922
## .Streptococcus_mitis.SGB8168.3m ~~
## .Str_.SGB8084.1 -0.100 0.055 -1.817
## .Enterococcus_avium.SGB7931.3m ~~
## .Str_.SGB8168.3 -0.586 0.170 -3.457
## .Eubacterium_rectale.SGB4933.3m ~~
## .Str_.SGB8168.3 0.174 0.039 4.447
## .Streptococcus_mitis.SGB8168.3m ~~
## .Rmn_.SGB4584.1 0.714 0.275 2.596
## .Enterococcus_avium.SGB7931.3m ~~
## .Vll_.SGB6936.1 -0.694 0.222 -3.123
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Fc_.SGB15316.1 0.514 0.176 2.918
## .Hungatella_hathewayi.SGB4742.3m ~~
## .Ent_.SGB7962.1 0.639 0.153 4.174
## .Bifidobacterium_dentium.SGB17234.1y ~~
## .Cls_.SGB4037.1 0.969 0.344 2.819
## .Bacteroides_caccae.SGB1877.3m ~~
## .Bct_.SGB1861.1 0.471 0.159 2.971
## .Bacteroides_thetaiotaomicron.SGB1861.3m ~~
## .Bct_.SGB1877.1 0.515 0.174 2.966
## .Ruminococcus_torques.SGB4608.3m ~~
## .Ent_.SGB7962.1 0.150 0.080 1.861
## .Mediterraneibacter_faecis.SGB4563.1y ~~
## .Phc_.SGB1814.1 1.183 0.411 2.878
## .Phocaeicola_vulgatus.SGB1814.1y ~~
## .Rt_.SGB15271.1 1.065 0.362 2.944
## .Ruthenibacterium_lactatiformans.SGB15271.3m ~~
## .Bct_.SGB1861.1 0.412 0.157 2.624
## .Ruminococcus_torques.SGB4608.1y ~~
## .Str_.SGB8084.1 -0.024 0.073 -0.323
## .Phocaeicola_dorei.SGB1815.3m ~~
## .Ent_.SGB7962.1 -1.944 0.327 -5.940
## .Enterococcus_faecalis.SGB7962.3m ~~
## .Fc_.SGB15316.3 -0.249 0.085 -2.926
## .Phocaeicola_vulgatus.SGB1814.1y ~~
## .St_.SGB8007_.1 1.214 0.415 2.924
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Ent_.SGB7962.1 0.212 0.069 3.076
## .Enterococcus_faecalis.SGB7962.3m ~~
## .Str_.SGB8065.3 1.199 0.415 2.891
## .Bacteroides_caccae.SGB1877.3m ~~
## .Fc_.SGB15316.3 0.103 0.039 2.659
## .Megasphaera_micronuciformis.SGB5868.3m ~~
## .Ent_.SGB7962.1 0.477 0.072 6.609
## .Mediterraneibacter_faecis.SGB4563.3m ~~
## .Str_.SGB8168.1 0.094 0.023 4.083
## .Eggerthella_lenta.SGB14809.3m ~~
## .Str_.SGB8168.1 0.357 0.077 4.620
## .Enterococcus_avium.SGB7931.3m ~~
## .Str_.SGB8168.1 -0.282 0.053 -5.295
## .Clostridium_innocuum.SGB4037.3m ~~
## .Str_.SGB8168.1 0.392 0.072 5.447
## .Ruminococcus_torques.SGB4608.3m ~~
## .Str_.SGB8168.1 0.044 0.020 2.186
## .Bacteroides_thetaiotaomicron.SGB1861.1y ~~
## .Str_.SGB8168.1 0.130 0.043 3.057
## .Streptococcus_thermophilus.SGB8002.3m ~~
## .Str_.SGB8168.1 0.149 0.040 3.729
## .Bacteroides_fragilis.SGB1855.1y ~~
## .Phc_.SGB1815.1 1.049 0.375 2.795
## .Faecalibacterium_prausnitzii.SGB15316.1y ~~
## .Rmn_.SGB4608.1 1.081 0.392 2.758
## .Eubacterium_rectale.SGB4933.1y ~~
## .St_.SGB8007_.1 1.100 0.395 2.785
## .Eggerthella_lenta.SGB14809.1y ~~
## .Rt_.SGB16985.1 -0.144 0.052 -2.780
## .Phc_.SGB1814.1 1.067 0.382 2.790
## .Megasphaera_micronuciformis.SGB5868.3m ~~
## .Str_.SGB8084.1 0.082 0.038 2.132
## .Rmn_.SGB4584.1 -1.475 0.219 -6.732
## .Mediterraneibacter_faecis.SGB4563.3m ~~
## .Str_.SGB8084.1 0.014 0.061 0.223
## .Streptococcus_peroris.SGB8084.1y ~~
## .St_.SGB8007_.1 0.012 0.066 0.185
## .Bacteroides_caccae.SGB1877.1y ~~
## .Rmn_.SGB4608.1 0.663 0.234 2.835
## .Enterococcus_faecalis.SGB7962.3m ~~
## .Phc_.SGB1814.3 -0.932 0.340 -2.744
## .Clostridium_sp_C5_48.SGB4752.3m ~~
## .Rt_.SGB16985.3 0.216 0.082 2.641
## .Phocaeicola_vulgatus.SGB1814.1y ~~
## .Rmn_.SGB4608.1 0.935 0.353 2.648
## .Eubacterium_rectale.SGB4933.3m ~~
## .Str_.SGB8005.1 0.127 0.049 2.569
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Ebc_.SGB4933.3 0.355 0.049 7.194
## .Eubacterium_rectale.SGB4933.3m ~~
## .Ent_.SGB4760.1 0.388 0.062 6.261
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Bct_.SGB1855.1 0.281 0.095 2.959
## .Megasphaera_micronuciformis.SGB5868.1y ~~
## .Str_.SGB8084.1 -0.008 0.047 -0.169
## .Hungatella_hathewayi.SGB4742.1y ~~
## .Mgs_.SGB5868.1 0.579 0.212 2.736
## .Enterococcus_avium.SGB7931.3m ~~
## .Mgs_.SGB5868.1 -0.525 0.192 -2.737
## .Bifidobacterium_longum.subsp.infantis.1y ~~
## .Rt_.SGB16985.1 0.151 0.056 2.717
## .Enterocloster_clostridioformis.SGB4760.1y ~~
## .Str_.SGB8002.1 0.836 0.282 2.970
## .Mediterraneibacter_faecis.SGB4563.1y ~~
## .Mgs_.SGB5868.1 -0.869 0.326 -2.666
## .Bacteroides_fragilis.SGB1855.3m ~~
## .Fc_.SGB15316.3 0.186 0.060 3.076
## .Enterococcus_avium.SGB7931.1y ~~
## .Rt_.SGB15271.1 0.537 0.217 2.474
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Phc_.SGB1815.3 -1.733 0.252 -6.874
## .Rothia_mucilaginosa.SGB16985.1y ~~
## .St_.SGB8007_.1 -0.180 0.068 -2.666
## .Bifidobacterium_dentium.SGB17234.3m ~~
## .Bct_.SGB1855.1 -1.091 0.423 -2.582
## .Bifidobacterium_animalis.SGB17278.1y ~~
## .Fc_.SGB15316.1 -1.528 0.579 -2.639
## .Streptococcus_peroris.SGB8084.1y ~~
## .Str_.SGB8002.1 0.138 0.085 1.624
## .Streptococcus_mitis.SGB8168.1y ~~
## .Str_.SGB8002.1 0.155 0.053 2.924
## .Enterococcus_avium.SGB7931.3m ~~
## .Ent_.SGB4760.1 -0.965 0.190 -5.065
## .Clostridium_innocuum.SGB4037.3m ~~
## .Ent_.SGB4760.1 1.205 0.239 5.035
## .Enterococcus_avium.SGB7931.3m ~~
## .Rmn_.SGB4608.1 0.424 0.189 2.248
## .Eubacterium_rectale.SGB4933.3m ~~
## .Mdt_.SGB4563.1 0.148 0.066 2.249
## .Veillonella_atypica.SGB6936.3m ~~
## .Str_.SGB8168.1 0.059 0.025 2.371
## .Bifidobacterium_dentium.SGB17234.3m ~~
## .Rt_.SGB15271.3 0.458 0.170 2.690
## .Ruthenibacterium_lactatiformans.SGB15271.3m ~~
## .Str_.SGB8168.3 -0.314 0.140 -2.245
## .Streptococcus_thermophilus.SGB8002.3m ~~
## .Phc_.SGB1814.1 0.652 0.256 2.546
## .Mediterraneibacter_faecis.SGB4563.1y ~~
## .Rt_.SGB15271.1 0.734 0.288 2.546
## .Streptococcus_salivarius.SGB8005.3m ~~
## .C__C5_48.SGB47 0.905 0.337 2.683
## .Faecalibacterium_prausnitzii.SGB15316.1y ~~
## .Phc_.SGB1814.1 1.408 0.538 2.618
## .Bifidobacterium_longum.subsp.infantis.1y ~~
## .Bf_.SGB17278.1 -1.422 0.510 -2.789
## .Mediterraneibacter_faecis.SGB4563.3m ~~
## .Ebc_.SGB4933.1 0.297 0.121 2.454
## .Bifidobacterium_dentium.SGB17234.3m ~~
## .Rmn_.SGB4584.1 -1.088 0.390 -2.792
## .Megasphaera_micronuciformis.SGB5868.3m ~~
## .Rmn_.SGB4608.3 -0.103 0.043 -2.378
## .Bifidobacterium_longum.subsp.infantis.1y ~~
## .Bf_.SGB17234.1 0.832 0.317 2.628
## .Clostridium_innocuum.SGB4037.1y ~~
## .Str_.SGB8168.1 0.092 0.057 1.617
## .Ruminococcus_torques.SGB4608.1y ~~
## .Str_.SGB8168.1 0.139 0.048 2.927
## .Enterococcus_avium.SGB7931.3m ~~
## .Cls_.SGB4037.1 0.201 0.221 0.909
## .Faecalibacterium_prausnitzii.SGB15316.1y ~~
## .Mgs_.SGB5868.1 -0.906 0.356 -2.544
## .Bacteroides_thetaiotaomicron.SGB1861.3m ~~
## .St_.SGB8007_.3 0.568 0.222 2.560
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .St_.SGB8007_.3 0.668 0.221 3.017
## .Eubacterium_rectale.SGB4933.3m ~~
## .St_.SGB8007_.3 0.157 0.062 2.540
## .Ruminococcus_gnavus.SGB4584.3m ~~
## .Eg_.SGB14809.1 1.029 0.400 2.576
## .Bacteroides_fragilis.SGB1855.3m ~~
## .Eg_.SGB14809.3 1.013 0.386 2.623
## .Phocaeicola_vulgatus.SGB1814.3m ~~
## .Bct_.SGB1861.1 0.625 0.234 2.670
## .Ruthenibacterium_lactatiformans.SGB15271.3m ~~
## .Vll_.SGB6936.1 -0.532 0.220 -2.416
## .Streptococcus_peroris.SGB8084.1y ~~
## .Vll_.SGB6936.1 0.073 0.057 1.273
## .Veillonella_atypica.SGB6936.3m ~~
## .Vll_.SGB6936.1 0.411 0.169 2.436
## .Bacteroides_fragilis.SGB1855.1y ~~
## .Vll_.SGB6936.1 0.755 0.315 2.399
## .Bacteroides_thetaiotaomicron.SGB1861.1y ~~
## .Fc_.SGB15316.1 0.987 0.389 2.539
## .Eggerthella_lenta.SGB14809.1y ~~
## .Phc_.SGB1815.1 0.789 0.315 2.503
## .Enterococcus_avium.SGB7931.3m ~~
## .Ent_.SGB7962.1 -0.458 0.142 -3.221
## .Mdt_.SGB4563.3 0.211 0.092 2.291
## .Bacteroides_fragilis.SGB1855.3m ~~
## .Bct_.SGB1861.1 0.728 0.291 2.502
## .Bacteroides_caccae.SGB1877.1y ~~
## .Ebc_.SGB4933.1 0.720 0.290 2.482
## .Phocaeicola_dorei.SGB1815.1y ~~
## .Rt_.SGB15271.1 0.711 0.278 2.554
## .Enterocloster_clostridioformis.SGB4760.1y ~~
## .Ent_.SGB7962.1 -0.796 0.177 -4.498
## .Bifidobacterium_longum.subsp.infantis.1y ~~
## .Ent_.SGB4760.1 -0.786 0.291 -2.704
## .Streptococcus_mitis.SGB8168.3m ~~
## .Ent_.SGB4760.1 -0.439 0.166 -2.651
## .Bifidobacterium_dentium.SGB17234.1y ~~
## .Mdt_.SGB4563.1 -0.979 0.396 -2.469
## .Enterocloster_clostridioformis.SGB4760.1y ~~
## .Phc_.SGB1814.1 0.715 0.256 2.792
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Ent_.SGB4760.1 0.233 0.059 3.975
## .Clostridium_innocuum.SGB4037.3m ~~
## .Mdt_.SGB4563.3 -0.208 0.090 -2.314
## .Mediterraneibacter_faecis.SGB4563.3m ~~
## .Bf_.SGB17278.1 -0.294 0.142 -2.062
## .Clostridium_innocuum.SGB4037.1y ~~
## .Mgs_.SGB5868.1 0.538 0.282 1.910
## .Bacteroides_caccae.SGB1877.3m ~~
## .Eg_.SGB14809.1 -0.426 0.175 -2.439
## .Bifidobacterium_dentium.SGB17234.3m ~~
## .Rt_.SGB15271.1 0.651 0.257 2.529
## .Eubacterium_rectale.SGB4933.1y ~~
## .Mgs_.SGB5868.1 -0.758 0.324 -2.339
## .Rothia_mucilaginosa.SGB16985.3m ~~
## .St_.SGB8007_.1 -0.657 0.250 -2.627
## .Enterococcus_faecalis.SGB7962.3m ~~
## .Phc_.SGB1815.3 -0.749 0.342 -2.193
## .Enterococcus_avium.SGB7931.3m ~~
## .Bf_.SGB17234.1 -0.556 0.235 -2.370
## .Eubacterium_rectale.SGB4933.3m ~~
## .Bf_.SGB17234.1 0.154 0.052 2.974
## .Bifidobacterium_dentium.SGB17234.1y ~~
## .Rmn_.SGB4584.1 0.514 0.297 1.727
## .Streptococcus_salivarius.SGB8007_group.3m ~~
## .Str_.SGB8168.1 0.110 0.043 2.566
## .Bacteroides_thetaiotaomicron.SGB1861.1y ~~
## .Eg_.SGB14809.1 0.668 0.275 2.433
## .Ruthenibacterium_lactatiformans.SGB15271.3m ~~
## .Mdt_.SGB4563.1 0.425 0.176 2.418
## .Bacteroides_caccae.SGB1877.1y ~~
## .Rt_.SGB15271.1 0.594 0.240 2.477
## .Streptococcus_thermophilus.SGB8002.3m ~~
## .Ebc_.SGB4933.1 -0.674 0.262 -2.573
## .Ruminococcus_torques.SGB4608.1y ~~
## .St_.SGB8007_.1 0.857 0.313 2.742
## .Phocaeicola_dorei.SGB1815.1y ~~
## .St_.SGB8007_.1 0.817 0.334 2.447
## .Bacteroides_caccae.SGB1877.1y ~~
## .Bf_.SGB17234.1 -0.715 0.296 -2.413
## .Clostridium_sp_C5_48.SGB4752.3m ~~
## .Rmn_.SGB4584.1 -1.973 0.283 -6.961
## .Str_.SGB8084.1 0.133 0.073 1.825
## .Enterococcus_avium.SGB7931.3m ~~
## .Str_.SGB8005.1 -0.191 0.190 -1.004
## .Bacteroides_caccae.SGB1877.3m ~~
## .Str_.SGB8168.1 0.059 0.022 2.651
## .Enterococcus_faecalis.SGB7962.3m ~~
## .Str_.SGB8002.3 0.570 0.239 2.383
## .Phocaeicola_dorei.SGB1815.3m ~~
## .Bct_.SGB1877.1 0.505 0.205 2.467
## .Enterococcus_avium.SGB7931.3m ~~
## .Phc_.SGB1815.3 -1.732 0.292 -5.925
## .Hungatella_hathewayi.SGB4742.3m ~~
## .Phc_.SGB1815.3 1.542 0.271 5.685
## .Mediterraneibacter_faecis.SGB4563.3m ~~
## .Phc_.SGB1815.3 0.558 0.140 3.986
## .Clostridium_innocuum.SGB4037.3m ~~
## .Phc_.SGB1815.3 2.364 0.395 5.989
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Phc_.SGB1815.3 1.339 0.282 4.748
## .Phocaeicola_dorei.SGB1815.3m ~~
## .Ent_.SGB4760.1 -1.964 0.321 -6.112
## .Bfdbctrm_l...1 -1.195 0.391 -3.053
## .Str_.SGB8002.3 0.964 0.256 3.766
## .Vll_.SGB6936.1 -0.767 0.279 -2.746
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Phc_.SGB1815.3 0.521 0.122 4.287
## .Phocaeicola_dorei.SGB1815.3m ~~
## .Str_.SGB8168.1 -0.554 0.115 -4.828
## .Clostridium_innocuum.SGB4037.3m ~~
## .Fc_.SGB15316.3 -0.229 0.068 -3.369
## .Phocaeicola_dorei.SGB1815.3m ~~
## .Rt_.SGB15271.3 -0.772 0.247 -3.128
## .Eggerthella_lenta.SGB14809.3m ~~
## .Str_.SGB8065.3 -1.179 0.405 -2.908
## .Streptococcus_lactarius.SGB8065.3m ~~
## .Mgs_.SGB5868.1 0.691 0.262 2.637
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Ent_.SGB4760.1 0.505 0.185 2.733
## .Bifidobacterium_longum.subsp.infantis.1y ~~
## .Mgs_.SGB5868.1 0.635 0.252 2.522
## .Ruminococcus_gnavus.SGB4584.3m ~~
## .St_.SGB8007_.3 1.036 0.389 2.665
## .Streptococcus_lactarius.SGB8065.3m ~~
## .Vll_.SGB6936.1 0.779 0.306 2.544
## .Phocaeicola_vulgatus.SGB1814.3m ~~
## .Phc_.SGB1815.1 -0.785 0.336 -2.341
## .Streptococcus_mitis.SGB8168.3m ~~
## .Str_.SGB8005.3 0.456 0.188 2.422
## .Phocaeicola_dorei.SGB1815.1y ~~
## .Str_.SGB8005.1 -0.833 0.351 -2.373
## .Bacteroides_thetaiotaomicron.SGB1861.1y ~~
## .St_.SGB8007_.1 0.755 0.300 2.516
## .Eggerthella_lenta.SGB14809.1y ~~
## .Fc_.SGB15316.1 1.037 0.442 2.347
## .Bifidobacterium_dentium.SGB17234.1y ~~
## .Eg_.SGB14809.1 0.677 0.291 2.330
## .Clostridium_sp_C5_48.SGB4752.3m ~~
## .Bct_.SGB1877.1 0.181 0.089 2.023
## .Bifidobacterium_animalis.SGB17278.1y ~~
## .St_.SGB8007_.1 1.050 0.426 2.463
## .Eubacterium_rectale.SGB4933.1y ~~
## .Phc_.SGB1815.1 0.823 0.353 2.330
## .Bifidobacterium_longum.subsp.infantis.1y ~~
## .Hng_.SGB4742.1 -0.891 0.353 -2.520
## .Ruminococcus_torques.SGB4608.3m ~~
## .Rt_.SGB15271.1 0.249 0.092 2.712
## .Ruminococcus_gnavus.SGB4584.3m ~~
## .Rt_.SGB15271.1 0.890 0.365 2.442
## .Streptococcus_lactarius.SGB8065.3m ~~
## .Rmn_.SGB4608.1 -0.820 0.334 -2.454
## .Bacteroides_caccae.SGB1877.3m ~~
## .Rmn_.SGB4584.3 0.538 0.240 2.246
## .Enterococcus_avium.SGB7931.1y ~~
## .Mdt_.SGB4563.1 -0.668 0.294 -2.268
## .Faecalibacterium_prausnitzii.SGB15316.1y ~~
## .Vll_.SGB6936.1 -0.890 0.392 -2.269
## .Bacteroides_caccae.SGB1877.3m ~~
## .Mdt_.SGB4563.1 0.407 0.183 2.227
## .Megasphaera_micronuciformis.SGB5868.1y ~~
## .Rt_.SGB15271.1 -0.600 0.266 -2.256
## .Bifidobacterium_animalis.SGB17278.1y ~~
## .Rt_.SGB15271.1 0.869 0.360 2.412
## .Eubacterium_rectale.SGB4933.1y ~~
## .Str_.SGB8084.1 -0.103 0.076 -1.345
## .Bifidobacterium_longum.subsp.infantis.3m ~~
## .Ent_.SGB7962.3 1.019 0.428 2.383
## .Enterocloster_clostridioformis.SGB4760.1y ~~
## .Ebc_.SGB4933.1 -0.468 0.276 -1.695
## .Enterococcus_avium.SGB7931.1y ~~
## .Ebc_.SGB4933.1 -0.649 0.300 -2.168
## .Phocaeicola_dorei.SGB1815.1y ~~
## .Str_.SGB8002.1 -0.859 0.381 -2.252
## .Ruminococcus_gnavus.SGB4584.3m ~~
## .Str_.SGB8065.3 -1.194 0.463 -2.577
## .Streptococcus_salivarius.SGB8007_group.3m ~~
## .Phc_.SGB1815.1 0.687 0.304 2.260
## .Clostridium_sp_C5_48.SGB4752.1y ~~
## .Eg_.SGB14809.1 0.748 0.323 2.320
## .Ruminococcus_gnavus.SGB4584.1y ~~
## .Rmn_.SGB4608.1 -0.307 0.414 -0.741
## .Enterococcus_faecalis.SGB7962.1y ~~
## .Str_.SGB8168.1 -0.184 0.043 -4.233
## .Hungatella_hathewayi.SGB4742.1y ~~
## .Str_.SGB8168.1 0.094 0.044 2.135
## .Bifidobacterium_dentium.SGB17234.3m ~~
## .Str_.SGB8168.1 0.187 0.055 3.403
## .Eubacterium_rectale.SGB4933.3m ~~
## .Vll_.SGB6936.3 0.058 0.024 2.406
## .Enterococcus_faecalis.SGB7962.1y ~~
## .St_.SGB8007_.1 0.300 0.208 1.443
## .Eubacterium_rectale.SGB4933.3m ~~
## .Phc_.SGB1814.1 0.096 0.064 1.488
## .Clostridium_sp_C5_48.SGB4752.1y ~~
## .Ebc_.SGB4933.1 -0.883 0.392 -2.254
## .Bacteroides_caccae.SGB1877.1y ~~
## .St_.SGB8007_.1 0.636 0.278 2.286
## .Bacteroides_thetaiotaomicron.SGB1861.3m ~~
## .Rt_.SGB16985.3 -0.488 0.219 -2.223
## .Bifidobacterium_longum.subsp.infantis.3m ~~
## .Phc_.SGB1814.3 -1.049 0.466 -2.252
## .Enterococcus_faecalis.SGB7962.3m ~~
## .Ebc_.SGB4933.3 -0.129 0.060 -2.143
## .Enterococcus_avium.SGB7931.3m ~~
## .Mdt_.SGB4563.1 0.404 0.175 2.317
## .Bifidobacterium_dentium.SGB17234.1y ~~
## .Fc_.SGB15316.1 -0.916 0.417 -2.194
## .Bacteroides_fragilis.SGB1855.3m ~~
## .Str_.SGB8005.3 0.739 0.338 2.186
## .Streptococcus_peroris.SGB8084.3m ~~
## .Vll_.SGB6936.3 0.254 0.116 2.183
## .Eggerthella_lenta.SGB14809.3m ~~
## .Rt_.SGB15271.1 0.747 0.304 2.458
## .Ruminococcus_torques.SGB4608.3m ~~
## .Cls_.SGB4037.1 0.434 0.148 2.935
## .Clostridium_sp_C5_48.SGB4752.3m ~~
## .Phc_.SGB1815.3 1.378 0.204 6.768
## .Ent_.SGB7962.1 0.575 0.104 5.532
## .Ent_.SGB4760.3 0.610 0.096 6.352
## .Ent_.SGB4760.1 0.679 0.120 5.650
## .Enterococcus_avium.SGB7931.3m ~~
## .Mgs_.SGB5868.3 0.450 0.089 5.075
## .Clostridium_innocuum.SGB4037.3m ~~
## .Mgs_.SGB5868.3 -0.575 0.105 -5.496
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Ent_.SGB7931.1 0.110 0.058 1.901
## .Ruminococcus_torques.SGB4608.3m ~~
## .St_.SGB8007_.3 0.244 0.103 2.364
## .Eggerthella_lenta.SGB14809.3m ~~
## .Phc_.SGB1814.3 0.770 0.312 2.463
## .Enterocloster_clostridioformis.SGB4760.1y ~~
## .Str_.SGB8168.1 -0.224 0.052 -4.293
## .Phocaeicola_dorei.SGB1815.3m ~~
## .Str_.SGB8005.1 -0.453 0.309 -1.466
## .Str_.SGB8168.3 -0.470 0.226 -2.078
## .Mgs_.SGB5868.1 -0.771 0.277 -2.779
## .Megasphaera_micronuciformis.SGB5868.1y ~~
## .Str_.SGB8168.1 -0.118 0.049 -2.428
## .Enterococcus_faecalis.SGB7962.1y ~~
## .Mgs_.SGB5868.1 -0.328 0.178 -1.839
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Bf_.SGB17278.1 0.768 0.276 2.782
## .Rothia_mucilaginosa.SGB16985.3m ~~
## .Rmn_.SGB4584.3 -0.802 0.320 -2.503
## .Eggerthella_lenta.SGB14809.3m ~~
## .Fc_.SGB15316.3 -0.084 0.084 -1.007
## .Hungatella_hathewayi.SGB4742.3m ~~
## .Vll_.SGB6936.3 0.251 0.109 2.296
## .Phocaeicola_vulgatus.SGB1814.3m ~~
## .Rmn_.SGB4584.3 0.815 0.345 2.362
## .Bacteroides_thetaiotaomicron.SGB1861.3m ~~
## .Phc_.SGB1815.1 0.442 0.210 2.108
## .Clostridium_innocuum.SGB4037.3m ~~
## .Fc_.SGB15316.1 -0.900 0.385 -2.339
## .Ruminococcus_gnavus.SGB4584.3m ~~
## .Cls_.SGB4037.1 1.328 0.468 2.835
## .Streptococcus_lactarius.SGB8065.3m ~~
## .C__C5_48.SGB47 0.762 0.347 2.194
## .Enterococcus_avium.SGB7931.3m ~~
## .Bfdbctrm_l...1 -0.383 0.251 -1.523
## .Streptococcus_peroris.SGB8084.3m ~~
## .Bf_.SGB17234.1 0.393 0.182 2.160
## .Eubacterium_rectale.SGB4933.1y ~~
## .Vll_.SGB6936.1 -0.668 0.343 -1.948
## .Bacteroides_thetaiotaomicron.SGB1861.3m ~~
## .Bfdbctrm_l...1 -0.664 0.312 -2.130
## .Bacteroides_caccae.SGB1877.1y ~~
## .Str_.SGB8005.1 -0.622 0.284 -2.188
## .Ruthenibacterium_lactatiformans.SGB15271.3m ~~
## .Mgs_.SGB5868.1 -0.619 0.310 -1.997
## .Bacteroides_caccae.SGB1877.3m ~~
## .Cls_.SGB4037.1 -0.430 0.212 -2.032
## .Ruthenibacterium_lactatiformans.SGB15271.3m ~~
## .Str_.SGB8005.3 0.372 0.179 2.073
## .Ruminococcus_torques.SGB4608.1y ~~
## .Vll_.SGB6936.1 -0.692 0.293 -2.366
## .Bifidobacterium_dentium.SGB17234.3m ~~
## .Phc_.SGB1815.3 0.983 0.330 2.976
## .Ent_.SGB7931.3 0.376 0.204 1.844
## .Fc_.SGB15316.3 -0.186 0.075 -2.478
## .Ent_.SGB4760.1 0.561 0.216 2.598
## .Mediterraneibacter_faecis.SGB4563.3m ~~
## .Ent_.SGB4760.1 0.194 0.118 1.634
## .Hungatella_hathewayi.SGB4742.3m ~~
## .Ent_.SGB4760.1 0.557 0.178 3.135
## .Enterocloster_clostridioformis.SGB4760.1y ~~
## .Rmn_.SGB4608.1 0.377 0.205 1.843
## .Megasphaera_micronuciformis.SGB5868.3m ~~
## .Ent_.SGB4760.1 0.471 0.090 5.250
## .Enterocloster_clostridioformis.SGB4760.1y ~~
## .Str_.SGB8005.1 -0.372 0.240 -1.552
## .Ruminococcus_torques.SGB4608.3m ~~
## .Ent_.SGB4760.1 0.055 0.097 0.569
## .Enterocloster_clostridioformis.SGB4760.1y ~~
## .Mgs_.SGB5868.1 -0.258 0.174 -1.482
## .Phocaeicola_dorei.SGB1815.3m ~~
## .Rmn_.SGB4608.1 0.476 0.302 1.578
## .Eggerthella_lenta.SGB14809.3m ~~
## .Bct_.SGB1861.1 0.717 0.288 2.491
## .Ruminococcus_torques.SGB4608.3m ~~
## .Str_.SGB8005.1 0.146 0.112 1.307
## .Clostridium_sp_C5_48.SGB4752.3m ~~
## .Str_.SGB8168.1 0.195 0.041 4.788
## .Hng_.SGB4742.3 -0.499 0.091 -5.480
## .Bifidobacterium_longum.subsp.infantis.1y ~~
## .Str_.SGB8168.1 -0.129 0.053 -2.427
## .Streptococcus_thermophilus.SGB8002.3m ~~
## .Bf_.SGB17278.1 0.697 0.272 2.559
## .Ruminococcus_gnavus.SGB4584.3m ~~
## .Phc_.SGB1814.1 0.862 0.468 1.841
## .Phocaeicola_dorei.SGB1815.3m ~~
## .Phc_.SGB1814.1 -0.467 0.356 -1.311
## .Streptococcus_peroris.SGB8084.3m ~~
## .Fc_.SGB15316.1 0.581 0.274 2.122
## .Hungatella_hathewayi.SGB4742.3m ~~
## .Str_.SGB8005.3 0.367 0.190 1.935
## .Bacteroides_caccae.SGB1877.3m ~~
## .Rt_.SGB15271.1 -0.405 0.193 -2.102
## .Rt_.SGB16985.3 -0.455 0.222 -2.053
## .Ruminococcus_gnavus.SGB4584.3m ~~
## .Bfdbctrm_l...1 -1.020 0.500 -2.038
## .Streptococcus_lactarius.SGB8065.3m ~~
## .Mdt_.SGB4563.1 -0.764 0.368 -2.075
## .Mediterraneibacter_faecis.SGB4563.3m ~~
## .Str_.SGB8002.1 -0.239 0.131 -1.820
## .Bacteroides_fragilis.SGB1855.3m ~~
## .Bfdbctrm_l...1 -0.960 0.465 -2.062
## .Streptococcus_salivarius.SGB8005.3m ~~
## .Bfdbctrm_l...1 0.745 0.356 2.093
## .Streptococcus_salivarius.SGB8007_group.3m ~~
## .C__C5_48.SGB47 -0.692 0.342 -2.025
## .Eubacterium_rectale.SGB4933.3m ~~
## .Hng_.SGB4742.1 -0.153 0.073 -2.098
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Hng_.SGB4742.1 -0.097 0.069 -1.402
## .Bifidobacterium_longum.subsp.infantis.1y ~~
## .Cls_.SGB4037.1 -0.929 0.402 -2.308
## .Bacteroides_caccae.SGB1877.3m ~~
## .Phc_.SGB1815.1 0.335 0.165 2.028
## .Eggerthella_lenta.SGB14809.3m ~~
## .Ebc_.SGB4933.3 -0.184 0.073 -2.535
## .Bacteroides_thetaiotaomicron.SGB1861.3m ~~
## .Rt_.SGB15271.1 -0.444 0.218 -2.033
## .Streptococcus_thermophilus.SGB8002.3m ~~
## .Vll_.SGB6936.3 0.277 0.123 2.250
## .Clostridium_sp_C5_48.SGB4752.3m ~~
## .Vll_.SGB6936.3 0.116 0.066 1.763
## .Mediterraneibacter_faecis.SGB4563.1y ~~
## .Vll_.SGB6936.1 -0.694 0.340 -2.045
## .Bifidobacterium_dentium.SGB17234.3m ~~
## .Fc_.SGB15316.1 -0.877 0.424 -2.066
## .Streptococcus_mitis.SGB8168.3m ~~
## .Ent_.SGB7962.1 -0.069 0.131 -0.526
## .Bacteroides_fragilis.SGB1855.3m ~~
## .Phc_.SGB1815.3 0.465 0.285 1.628
## .Ruminococcus_torques.SGB4608.3m ~~
## .Bct_.SGB1877.1 0.173 0.080 2.170
## .Enterocloster_clostridioformis.SGB4760.1y ~~
## .Rt_.SGB15271.1 0.389 0.194 1.999
## .Bacteroides_thetaiotaomicron.SGB1861.1y ~~
## .Ent_.SGB4760.1 0.289 0.182 1.590
## .Bifidobacterium_dentium.SGB17234.3m ~~
## .Str_.SGB8065.3 -0.726 0.357 -2.034
## .Eggerthella_lenta.SGB14809.3m ~~
## .Bf_.SGB17234.1 -0.728 0.333 -2.188
## .Phocaeicola_vulgatus.SGB1814.1y ~~
## .Str_.SGB8065.1 0.435 0.216 2.010
## .Bacteroides_caccae.SGB1877.1y ~~
## .Mdt_.SGB4563.1 0.522 0.265 1.969
## .Streptococcus_lactarius.SGB8065.3m ~~
## .Bct_.SGB1861.1 -0.652 0.319 -2.045
## .Str_.SGB8005.1 0.672 0.326 2.063
## .Ruminococcus_torques.SGB4608.3m ~~
## .Ebc_.SGB4933.1 0.175 0.108 1.623
## .Enterococcus_faecalis.SGB7962.1y ~~
## .Ebc_.SGB4933.1 -0.240 0.232 -1.033
## .Bacteroides_caccae.SGB1877.3m ~~
## .Rt_.SGB15271.3 0.216 0.103 2.099
## .Streptococcus_mitis.SGB8168.3m ~~
## .Eg_.SGB14809.1 -0.355 0.186 -1.905
## .Clostridium_sp_C5_48.SGB4752.3m ~~
## .Eg_.SGB14809.1 0.293 0.139 2.111
## .Ebc_.SGB4933.3 -0.264 0.048 -5.561
## .Bifidobacterium_longum.subsp.infantis.1y ~~
## .Rt_.SGB15271.1 -0.720 0.368 -1.956
## .Faecalibacterium_prausnitzii.SGB15316.1y ~~
## .Rt_.SGB15271.1 0.758 0.380 1.992
## .Streptococcus_peroris.SGB8084.3m ~~
## .Bfdbctrm_l...1 0.427 0.212 2.013
## .Clostridium_sp_C5_48.SGB4752.1y ~~
## .St_.SGB8007_.1 0.679 0.331 2.049
## .Bacteroides_caccae.SGB1877.3m ~~
## .Bct_.SGB1855.3 0.378 0.187 2.017
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Fc_.SGB15316.1 -0.253 0.195 -1.294
## .Bifidobacterium_longum.subsp.infantis.1y ~~
## .Str_.SGB8002.1 -0.937 0.469 -1.997
## .Bifidobacterium_animalis.SGB17278.1y ~~
## .Mdt_.SGB4563.1 -0.786 0.398 -1.973
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Fc_.SGB15316.1 -0.679 0.341 -1.989
## .Streptococcus_salivarius.SGB8005.3m ~~
## .Str_.SGB8065.1 0.332 0.176 1.883
## .Bacteroides_fragilis.SGB1855.1y ~~
## .Bct_.SGB1861.1 0.663 0.321 2.065
## .Clostridium_innocuum.SGB4037.3m ~~
## .Str_.SGB8065.3 -0.712 0.312 -2.279
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Str_.SGB8002.3 1.070 0.127 8.423
## .Mdt_.SGB4563.3 0.403 0.062 6.481
## .Hng_.SGB4742.3 0.864 0.126 6.848
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Ent_.SGB4760.3 0.841 0.122 6.919
## .Eggerthella_lenta.SGB14809.3m ~~
## .Ent_.SGB4760.3 1.601 0.258 6.206
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Rmn_.SGB4608.3 0.260 0.054 4.850
## .Mgs_.SGB5868.3 0.495 0.067 7.367
## .Rothia_mucilaginosa.SGB16985.3m ~~
## .Str_.SGB8065.3 4.127 0.575 7.177
## .Clostridium_innocuum.SGB4037.1y ~~
## .Hng_.SGB4742.1 3.459 0.514 6.734
## .Streptococcus_peroris.SGB8084.1y ~~
## .Str_.SGB8005.1 0.610 0.086 7.065
## .Streptococcus_salivarius.SGB8007_group.3m ~~
## .Vll_.SGB6936.3 1.327 0.205 6.469
## .Megasphaera_micronuciformis.SGB5868.1y ~~
## .Vll_.SGB6936.1 2.077 0.319 6.519
## .Megasphaera_micronuciformis.SGB5868.3m ~~
## .Vll_.SGB6936.3 0.271 0.049 5.575
## .Bifidobacterium_longum.subsp.infantis.3m ~~
## .Rt_.SGB16985.3 1.700 0.295 5.757
## .Bifidobacterium_dentium.SGB17234.1y ~~
## .Mgs_.SGB5868.1 1.636 0.286 5.723
## .Rothia_mucilaginosa.SGB16985.3m ~~
## .Str_.SGB8168.3 0.898 0.161 5.563
## .Clostridium_innocuum.SGB4037.1y ~~
## .Str_.SGB8002.1 2.958 0.551 5.365
## .Rothia_mucilaginosa.SGB16985.3m ~~
## .Bfdbctrm_l...1 1.565 0.286 5.473
## .Megasphaera_micronuciformis.SGB5868.3m ~~
## .Phc_.SGB1814.3 0.434 0.076 5.688
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Rmn_.SGB4608.1 0.660 0.148 4.451
## .Hungatella_hathewayi.SGB4742.1y ~~
## .Rmn_.SGB4584.1 1.652 0.315 5.242
## .Clostridium_sp_C5_48.SGB4752.3m ~~
## .Fc_.SGB15316.3 -0.021 0.036 -0.567
## .Ruminococcus_gnavus.SGB4584.1y ~~
## .St_.SGB8007_.1 1.890 0.382 4.946
## .Megasphaera_micronuciformis.SGB5868.3m ~~
## .Str_.SGB8168.1 0.141 0.030 4.731
## .Eubacterium_rectale.SGB4933.3m ~~
## .Mgs_.SGB5868.3 -0.123 0.025 -4.846
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Mdt_.SGB4563.3 0.071 0.021 3.370
## .Megasphaera_micronuciformis.SGB5868.3m ~~
## .Str_.SGB8002.3 -0.198 0.052 -3.801
## .Streptococcus_lactarius.SGB8065.3m ~~
## .Str_.SGB8084.3 1.052 0.233 4.518
## .Bacteroides_thetaiotaomicron.SGB1861.3m ~~
## .Str_.SGB8168.1 0.088 0.024 3.697
## .Bifidobacterium_animalis.SGB17278.1y ~~
## .Rmn_.SGB4584.1 1.949 0.444 4.389
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Str_.SGB8168.3 -0.388 0.120 -3.227
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Rmn_.SGB4608.3 0.309 0.061 5.059
## .Eggerthella_lenta.SGB14809.3m ~~
## .Mgs_.SGB5868.3 -0.268 0.105 -2.540
## .Enterococcus_avium.SGB7931.1y ~~
## .Str_.SGB8084.1 0.157 0.035 4.510
## .Bifidobacterium_dentium.SGB17234.3m ~~
## .Ent_.SGB4760.3 0.489 0.162 3.011
## .Clostridium_sp_C5_48.SGB4752.3m ~~
## .Rt_.SGB15271.3 0.361 0.070 5.168
## .Clostridium_sp_C5_48.SGB4752.1y ~~
## .Ent_.SGB4760.1 0.815 0.217 3.762
## .Clostridium_innocuum.SGB4037.1y ~~
## .Rmn_.SGB4608.1 1.443 0.371 3.889
## .Ruthenibacterium_lactatiformans.SGB15271.1y ~~
## .St_.SGB8007_.1 1.285 0.344 3.738
## .Enterococcus_avium.SGB7931.1y ~~
## .Mgs_.SGB5868.1 0.716 0.188 3.816
## .Ruminococcus_gnavus.SGB4584.3m ~~
## .Str_.SGB8002.3 1.464 0.294 4.981
## .Enterocloster_clostridioformis.SGB4760.1y ~~
## .St_.SGB8007_.1 0.875 0.248 3.528
## .Clostridium_innocuum.SGB4037.1y ~~
## .Mdt_.SGB4563.1 -1.373 0.388 -3.542
## .Mediterraneibacter_faecis.SGB4563.3m ~~
## .Mgs_.SGB5868.3 -0.121 0.041 -2.982
## .Eubacterium_rectale.SGB4933.1y ~~
## .Hng_.SGB4742.1 -1.393 0.394 -3.535
## .Veillonella_atypica.SGB6936.3m ~~
## .Mgs_.SGB5868.1 0.480 0.136 3.534
## .Clostridium_innocuum.SGB4037.3m ~~
## .Rmn_.SGB4584.3 1.593 0.371 4.295
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Ebc_.SGB4933.3 -0.022 0.036 -0.608
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Mgs_.SGB5868.3 -0.089 0.027 -3.319
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Mgs_.SGB5868.1 -0.434 0.174 -2.496
## .Megasphaera_micronuciformis.SGB5868.3m ~~
## .Rmn_.SGB4608.1 -0.287 0.101 -2.833
## .Bacteroides_thetaiotaomicron.SGB1861.3m ~~
## .Rmn_.SGB4584.3 0.994 0.275 3.617
## .Streptococcus_salivarius.SGB8005.3m ~~
## .Vll_.SGB6936.3 0.656 0.191 3.440
## .Clostridium_sp_C5_48.SGB4752.1y ~~
## .Hng_.SGB4742.1 1.031 0.303 3.399
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Vll_.SGB6936.1 -0.452 0.142 -3.176
## .Bifidobacterium_dentium.SGB17234.1y ~~
## .Str_.SGB8065.1 0.552 0.164 3.357
## .Enterococcus_avium.SGB7931.1y ~~
## .Hng_.SGB4742.1 0.738 0.218 3.389
## .Ruthenibacterium_lactatiformans.SGB15271.3m ~~
## .Bct_.SGB1877.1 0.455 0.137 3.329
## .Eubacterium_rectale.SGB4933.3m ~~
## .Phc_.SGB1814.3 0.130 0.039 3.346
## .Streptococcus_salivarius.SGB8007_group.3m ~~
## .Bfdbctrm_l...1 -1.359 0.407 -3.336
## .Streptococcus_peroris.SGB8084.3m ~~
## .Str_.SGB8005.1 0.642 0.193 3.320
## .Bacteroides_fragilis.SGB1855.1y ~~
## .Hng_.SGB4742.1 1.155 0.346 3.342
## .Bacteroides_thetaiotaomicron.SGB1861.3m ~~
## .Hng_.SGB4742.3 0.408 0.119 3.412
## .Clostridium_innocuum.SGB4037.1y ~~
## .Ebc_.SGB4933.1 -1.413 0.428 -3.303
## .Bifidobacterium_longum.subsp.infantis.3m ~~
## .Rmn_.SGB4584.3 -1.951 0.610 -3.197
## .Bacteroides_thetaiotaomicron.SGB1861.3m ~~
## .Eg_.SGB14809.3 0.842 0.250 3.372
## .Clostridium_innocuum.SGB4037.1y ~~
## .Fc_.SGB15316.1 -1.722 0.526 -3.276
## .Bacteroides_thetaiotaomicron.SGB1861.3m ~~
## .Phc_.SGB1814.1 0.892 0.273 3.265
## .Bifidobacterium_animalis.SGB17278.1y ~~
## .Eg_.SGB14809.1 1.330 0.408 3.261
## .Enterococcus_avium.SGB7931.1y ~~
## .Str_.SGB8065.1 0.412 0.127 3.250
## .Bacteroides_thetaiotaomicron.SGB1861.1y ~~
## .Rt_.SGB15271.1 0.838 0.260 3.231
## .Bifidobacterium_animalis.SGB17278.1y ~~
## .Hng_.SGB4742.1 1.204 0.371 3.246
## .Bacteroides_fragilis.SGB1855.1y ~~
## .Fc_.SGB15316.1 1.917 0.590 3.249
## .Eggerthella_lenta.SGB14809.1y ~~
## .Ent_.SGB4760.1 0.771 0.233 3.311
## .Eubacterium_rectale.SGB4933.1y ~~
## .Rmn_.SGB4584.1 -1.527 0.424 -3.600
## .Ruthenibacterium_lactatiformans.SGB15271.3m ~~
## .Phc_.SGB1814.1 0.697 0.214 3.259
## .Bacteroides_caccae.SGB1877.1y ~~
## .Fc_.SGB15316.1 1.207 0.377 3.202
## .Faecalibacterium_prausnitzii.SGB15316.1y ~~
## .Phc_.SGB1815.1 1.503 0.473 3.177
## .Streptococcus_mitis.SGB8168.1y ~~
## .Str_.SGB8005.1 0.155 0.045 3.423
## .Bifidobacterium_longum.subsp.infantis.1y ~~
## .St_.SGB8007_.1 -1.294 0.413 -3.131
## .Bifidobacterium_longum.subsp.infantis.3m ~~
## .St_.SGB8007_.3 -1.334 0.428 -3.118
## .Hungatella_hathewayi.SGB4742.1y ~~
## .Mdt_.SGB4563.1 -1.061 0.341 -3.113
## .Ruminococcus_gnavus.SGB4584.3m ~~
## .Bct_.SGB1877.1 1.059 0.340 3.117
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Bct_.SGB1877.1 0.165 0.054 3.044
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Bct_.SGB1861.1 0.296 0.120 2.470
## .Eggerthella_lenta.SGB14809.3m ~~
## .Rt_.SGB16985.3 -0.894 0.281 -3.182
## .Ruminococcus_torques.SGB4608.3m ~~
## .Vll_.SGB6936.3 -0.240 0.078 -3.093
## .Bifidobacterium_longum.subsp.infantis.3m ~~
## .Str_.SGB8005.3 1.146 0.374 3.063
## .Eggerthella_lenta.SGB14809.3m ~~
## .Str_.SGB8168.3 -0.949 0.239 -3.970
## .Enterococcus_avium.SGB7931.1y ~~
## .Ent_.SGB7962.1 0.398 0.135 2.943
## .Ruminococcus_torques.SGB4608.3m ~~
## .Bct_.SGB1861.1 0.210 0.071 2.970
## .Megasphaera_micronuciformis.SGB5868.3m ~~
## .Rt_.SGB15271.3 0.127 0.047 2.723
## .Eubacterium_rectale.SGB4933.3m ~~
## .Rmn_.SGB4584.3 -0.192 0.091 -2.104
## .Megasphaera_micronuciformis.SGB5868.3m ~~
## .Rmn_.SGB4584.3 -0.211 0.119 -1.778
## .Mediterraneibacter_faecis.SGB4563.3m ~~
## .Rmn_.SGB4584.3 -0.231 0.152 -1.516
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Hng_.SGB4742.3 -0.086 0.046 -1.857
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Bfdbctrm_l...1 -0.369 0.181 -2.041
## .Str_.SGB8005.1 -0.387 0.196 -1.977
## .Phocaeicola_dorei.SGB1815.3m ~~
## .Ebc_.SGB4933.1 0.680 0.316 2.153
## .Bifidobacterium_longum.subsp.infantis.3m ~~
## .Phc_.SGB1815.3 -0.827 0.383 -2.159
## .Enterocloster_clostridioformis.SGB4760.3m ~~
## .Hng_.SGB4742.1 0.282 0.143 1.965
## .Rt_.SGB15271.1 0.238 0.119 2.007
## .Streptococcus_thermophilus.SGB8002.3m ~~
## .Cls_.SGB4037.1 0.590 0.255 2.316
## .Phocaeicola_vulgatus.SGB1814.1y ~~
## .Str_.SGB8168.1 0.113 0.051 2.199
## .Eggerthella_lenta.SGB14809.3m ~~
## .Hng_.SGB4742.3 0.894 0.215 4.162
## .Cls_.SGB4037.1 0.829 0.376 2.203
## .Bifidobacterium_dentium.SGB17234.1y ~~
## .Str_.SGB8084.1 0.117 0.052 2.277
## .Hungatella_hathewayi.SGB4742.1y ~~
## .Str_.SGB8002.1 0.761 0.357 2.131
## .Enterococcus_avium.SGB7931.1y ~~
## .Rmn_.SGB4608.1 -0.509 0.240 -2.119
## .Clostridium_innocuum.SGB4037.3m ~~
## .Ent_.SGB7962.3 -0.622 0.313 -1.989
## .Bacteroides_fragilis.SGB1855.1y ~~
## .Rmn_.SGB4584.1 0.975 0.427 2.280
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Str_.SGB8065.3 -0.636 0.311 -2.044
## .Rothia_mucilaginosa.SGB16985.3m ~~
## .Cls_.SGB4037.1 -0.508 0.253 -2.006
## .Enterococcus_avium.SGB7931.3m ~~
## .Bct_.SGB1855.1 -0.514 0.262 -1.964
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .St_.SGB8007_.1 0.505 0.248 2.040
## .Streptococcus_lactarius.SGB8065.3m ~~
## .Str_.SGB8002.3 -0.532 0.269 -1.975
## .Streptococcus_peroris.SGB8084.3m ~~
## .Vll_.SGB6936.1 0.340 0.177 1.928
## .Faecalibacterium_prausnitzii.SGB15316.3m ~~
## .Bct_.SGB1861.1 0.111 0.054 2.047
## .Bifidobacterium_animalis.SGB17278.3m ~~
## .Cls_.SGB4037.1 0.657 0.254 2.585
## .Bifidobacterium_dentium.SGB17234.3m ~~
## .Ent_.SGB7962.1 0.129 0.227 0.568
## .Streptococcus_mitis.SGB8168.3m ~~
## .Vll_.SGB6936.1 0.318 0.171 1.861
## .overweight ~~
## .asthma5y_ep -0.088 0.082 -1.067
## .Tscore_abnorml -0.138 0.094 -1.469
## .atopy5y -0.126 0.060 -2.094
## .asthma5y_ep ~~
## .Tscore_abnorml 0.126 0.100 1.251
## .atopy5y 0.246 0.072 3.414
## .Tscore_abnormal ~~
## .atopy5y 0.097 0.081 1.205
## P(>|z|) Std.lv Std.all
##
## 0.021 0.690 0.081
##
## 0.000 0.964 0.221
##
## 0.000 0.545 0.122
##
## 0.075 0.384 0.056
##
## 0.009 0.210 0.082
##
## 0.226 -0.058 -0.029
##
## 0.003 -1.782 -1.489
##
## 0.000 0.604 0.156
##
## 0.660 0.083 0.012
##
## 0.933 -0.003 -0.003
##
## 0.134 0.059 0.035
##
## 0.002 0.973 0.091
##
## 0.400 0.265 0.024
##
## 0.094 0.476 0.051
##
## 0.000 0.660 2.807
##
## 0.000 0.279 0.437
##
## 0.000 0.064 0.165
##
## 0.000 0.574 0.385
##
## 0.000 0.649 0.411
## 0.000 0.050 0.123
##
## 0.000 0.438 0.255
## 0.000 0.139 0.118
##
## 0.000 2.911 0.480
##
## 0.000 0.567 0.222
##
## 0.000 4.678 0.793
##
## 0.000 1.820 0.404
##
## 0.000 0.140 0.068
## 0.000 0.155 0.351
##
## 0.392 -0.102 -0.040
##
## 0.000 2.373 0.417
##
## 0.000 4.393 4.127
##
## 0.000 1.390 0.246
##
## 0.000 2.979 0.432
##
## 0.000 1.821 0.388
##
## 0.010 0.047 0.037
## 0.500 -0.013 -0.015
##
## 0.000 2.859 3.055
##
## 0.000 3.722 0.342
##
## 0.000 0.654 0.204
##
## 0.000 5.888 0.396
##
## 0.000 1.564 0.252
##
## 0.000 0.327 0.330
##
## 0.000 -0.161 -0.121
##
## 0.000 4.961 0.435
##
## 0.000 0.244 0.103
##
## 0.000 1.210 0.312
##
## 0.000 1.498 0.292
##
## 0.000 0.736 0.283
##
## 0.000 6.153 0.377
##
## 0.000 0.871 0.184
##
## 0.000 0.886 0.175
##
## 0.000 -3.831 -0.335
##
## 0.000 0.913 0.555
##
## 0.000 1.193 0.268
##
## 0.000 5.945 0.448
##
## 0.000 0.829 0.174
##
## 0.000 2.043 0.227
##
## 0.000 1.399 0.206
##
## 0.000 1.667 0.283
## 0.000 4.060 6.625
##
## 0.000 1.522 0.204
##
## 0.000 0.356 0.138
##
## 0.000 2.090 0.260
##
## 0.000 0.597 0.107
##
## 0.000 0.536 0.152
##
## 0.000 0.106 0.111
##
## 0.201 0.082 0.034
##
## 0.000 2.386 0.261
##
## 0.000 1.840 0.201
##
## 0.000 2.089 0.209
##
## 0.000 -4.017 -0.368
##
## 0.000 0.431 0.263
##
## 0.000 2.234 0.259
##
## 0.000 0.075 0.056
##
## 0.000 -2.130 -0.197
##
## 0.000 2.907 0.278
##
## 0.000 2.264 0.221
##
## 0.000 1.004 0.251
## 0.000 0.333 0.146
##
## 0.000 1.571 0.220
##
## 0.000 1.066 0.187
##
## 0.000 1.660 0.259
##
## 0.000 -1.934 -1.893
##
## 0.000 0.403 0.155
##
## 0.000 1.588 0.158
##
## 0.000 2.515 0.242
## 0.000 2.213 0.184
##
## 0.000 0.289 0.120
##
## 0.000 1.643 0.178
##
## 0.000 2.795 0.222
##
## 0.000 2.602 0.218
##
## 0.000 0.474 0.307
##
## 0.000 1.849 0.202
##
## 0.000 0.265 0.109
##
## 0.000 1.390 0.172
##
## 0.000 -0.223 -0.361
##
## 0.000 0.112 0.221
##
## 0.000 0.863 0.110
##
## 0.000 1.313 0.165
##
## 0.000 0.588 0.153
##
## 0.000 0.984 0.138
##
## 0.000 1.372 0.180
##
## 0.000 0.396 0.119
##
## 0.000 1.322 0.170
##
## 0.000 1.691 0.559
##
## 0.000 2.599 0.184
##
## 0.001 -0.404 -0.126
##
## 0.000 2.355 0.170
##
## 0.000 1.106 0.149
##
## 0.000 0.558 0.126
##
## 0.000 1.883 0.173
##
## 0.000 0.620 0.119
##
## 0.000 2.063 0.157
##
## 0.000 1.793 1.459
##
## 0.000 1.757 1.759
##
## 0.000 1.577 0.142
##
## 0.029 -0.137 -0.068
##
## 0.000 2.155 0.160
##
## 0.000 2.080 0.188
##
## 0.000 2.244 0.162
##
## 0.000 0.598 0.122
##
## 0.000 1.190 0.147
##
## 0.000 2.143 0.184
##
## 0.000 -1.392 -0.181
##
## 0.000 1.123 0.138
##
## 0.000 0.212 0.101
##
## 0.000 0.263 0.097
##
## 0.000 1.781 0.310
##
## 0.000 0.371 0.209
##
## 0.000 1.094 0.129
##
## 0.000 0.384 0.098
##
## 0.000 1.266 0.140
##
## 0.000 0.210 0.106
##
## 0.000 1.079 1.387
##
## 0.000 1.800 0.262
##
## 0.000 0.508 0.258
##
## 0.000 1.009 0.126
##
## 0.000 1.519 0.168
##
## 0.001 0.735 0.096
##
## 0.000 0.600 0.104
##
## 0.000 0.473 0.108
##
## 0.000 1.170 0.136
##
## 0.000 0.184 0.108
##
## 0.000 1.081 0.111
##
## 0.000 1.204 0.121
##
## 0.000 -0.156 -0.088
##
## 0.000 0.823 0.113
##
## 0.000 0.882 0.117
##
## 0.000 0.563 0.108
##
## 0.000 1.229 0.124
##
## 0.000 1.045 0.121
##
## 0.017 0.470 0.066
##
## 0.095 -0.098 -0.039
##
## 0.000 -0.239 -0.110
##
## 0.332 0.072 0.022
##
## 0.000 1.768 0.359
##
## 0.070 0.110 0.042
##
## 0.000 0.839 0.100
##
## 0.000 1.082 0.119
##
## 0.000 1.154 0.218
##
## 0.000 0.538 0.121
##
## 0.000 0.911 0.195
##
## 0.000 0.499 0.099
##
## 0.000 -0.685 -0.105
##
## 0.000 -1.061 -0.123
##
## 0.000 1.139 0.108
##
## 0.000 0.601 0.106
##
## 0.000 1.464 0.116
##
## 0.000 2.248 0.142
##
## 0.000 -1.661 -0.135
##
## 0.370 0.082 0.020
##
## 0.005 0.074 0.040
##
## 0.855 -0.009 -0.003
##
## 0.001 1.220 0.101
##
## 0.000 0.796 0.099
##
## 0.133 0.098 0.028
##
## 0.001 0.535 0.096
##
## 0.001 1.362 0.117
##
## 0.013 0.423 0.063
##
## 0.002 0.119 0.055
##
## 0.001 0.397 0.089
##
## 0.002 0.133 0.054
##
## 0.002 1.498 0.104
##
## 0.001 0.169 0.074
##
## 0.000 3.102 0.424
##
## 0.000 1.537 0.365
##
## 0.001 -0.235 -0.175
## 0.000 2.899 5.531
##
## 0.000 -0.423 -0.294
## 0.000 6.174 11.050
##
## 0.001 0.287 0.111
##
## 0.000 0.968 6.440
##
## 0.000 -0.191 -0.197
## 0.004 0.153 0.120
##
## 0.004 -0.166 -0.105
##
## 0.000 1.677 0.209
## 0.086 0.184 0.060
##
## 0.000 1.491 1.687
##
## 0.003 0.150 0.097
##
## 0.000 0.910 0.263
##
## 0.000 0.854 0.201
##
## 0.000 2.808 7.456
##
## 0.058 0.113 0.050
##
## 0.119 0.071 0.039
## 0.000 0.664 0.156
##
## 0.000 1.175 0.629
##
## 0.000 2.478 0.405
##
## 0.000 -0.834 -0.386
##
## 0.074 -0.081 -0.057
##
## 0.000 -0.782 -1.833
##
## 0.002 0.444 0.078
##
## 0.013 0.202 0.107
## 0.000 1.324 0.296
##
## 0.024 0.128 0.051
##
## 0.000 2.254 4.046
##
## 0.000 -0.707 -0.308
##
## 0.000 0.763 0.169
##
## 0.000 -2.458 -2.451
## 0.000 1.108 0.181
## 0.000 1.319 0.196
##
## 0.000 -1.670 -2.253
## 0.387 0.071 0.037
## 0.000 0.253 0.208
##
## 0.000 -3.187 -4.336
##
## 0.030 0.813 0.850
##
## 0.000 -1.885 -3.124
##
## 0.000 -1.124 -4.179
## 0.009 0.067 0.098
##
## 0.989 0.001 0.000
##
## 0.812 0.014 0.005
##
## 0.000 1.110 1.902
## 0.138 -0.096 -0.064
##
## 0.788 -0.012 -0.006
##
## 0.007 -0.887 -1.273
##
## 0.043 0.109 0.049
##
## 0.000 1.527 1.978
##
## 0.001 0.996 0.137
##
## 0.001 0.619 0.115
##
## 0.003 0.112 0.060
##
## 0.003 0.743 0.088
##
## 0.001 -0.186 -0.073
##
## 0.001 -1.603 -1.247
##
## 0.109 0.040 0.050
## 0.000 -0.756 -2.442
##
## 0.850 0.007 0.007
## 0.172 -0.197 -0.494
##
## 0.002 -0.242 -0.097
##
## 0.002 0.655 0.091
##
## 0.000 0.739 0.323
##
## 0.003 0.818 0.137
##
## 0.069 -0.100 -0.062
##
## 0.001 -0.586 -0.161
##
## 0.000 0.174 0.093
##
## 0.009 0.714 1.126
##
## 0.002 -0.694 -0.137
##
## 0.004 0.514 0.118
##
## 0.000 0.639 0.174
##
## 0.005 0.969 0.088
##
## 0.003 0.471 0.078
##
## 0.003 0.515 0.079
##
## 0.063 0.150 0.062
##
## 0.004 1.183 0.091
##
## 0.003 1.065 0.094
##
## 0.009 0.412 0.075
##
## 0.747 -0.024 -0.010
##
## 0.000 -1.944 -0.571
##
## 0.003 -0.249 -0.066
##
## 0.003 1.214 0.092
##
## 0.002 0.212 0.113
##
## 0.004 1.199 0.091
##
## 0.008 0.103 0.048
##
## 0.000 0.477 0.256
##
## 0.000 0.094 0.134
##
## 0.000 0.357 0.217
##
## 0.000 -0.282 -0.327
##
## 0.000 0.392 0.324
##
## 0.029 0.044 0.066
##
## 0.002 0.130 0.092
##
## 0.000 0.149 0.130
##
## 0.005 1.049 0.088
##
## 0.006 1.081 0.087
##
## 0.005 1.100 0.086
##
## 0.005 -0.144 -0.057
## 0.005 1.067 0.089
##
## 0.033 0.082 0.104
## 0.000 -1.475 -4.821
##
## 0.823 0.014 0.012
##
## 0.854 0.012 0.005
##
## 0.005 0.663 0.084
##
## 0.006 -0.932 -0.085
##
## 0.008 0.216 0.069
##
## 0.008 0.935 0.083
##
## 0.010 0.127 0.045
##
## 0.000 0.355 0.321
##
## 0.000 0.388 0.215
##
## 0.003 0.281 0.073
##
## 0.866 -0.008 -0.004
##
## 0.006 0.579 0.078
##
## 0.006 -0.525 -0.119
##
## 0.007 0.151 0.048
##
## 0.003 0.836 0.102
##
## 0.008 -0.869 -0.101
##
## 0.002 0.186 0.051
##
## 0.013 0.537 0.071
##
## 0.000 -1.733 -0.753
##
## 0.008 -0.180 -0.064
##
## 0.010 -1.091 -0.091
##
## 0.008 -1.528 -0.090
##
## 0.104 0.138 0.044
##
## 0.003 0.155 0.077
##
## 0.000 -0.965 -0.275
##
## 0.000 1.205 0.245
##
## 0.025 0.424 0.084
##
## 0.024 0.148 0.049
##
## 0.018 0.059 0.056
##
## 0.007 0.458 0.074
##
## 0.025 -0.314 -0.077
##
## 0.011 0.652 0.073
##
## 0.011 0.734 0.074
##
## 0.007 0.905 0.081
##
## 0.009 1.408 0.086
##
## 0.005 -1.422 -0.093
##
## 0.014 0.297 0.057
##
## 0.005 -1.088 -1.120
##
## 0.017 -0.103 -0.077
##
## 0.009 0.832 0.070
##
## 0.106 0.092 0.053
##
## 0.003 0.139 0.096
##
## 0.364 0.201 0.033
##
## 0.011 -0.906 -0.084
##
## 0.010 0.568 0.073
##
## 0.003 0.668 0.084
##
## 0.011 0.157 0.055
##
## 0.010 1.029 0.084
##
## 0.009 1.013 0.086
##
## 0.008 0.625 0.074
##
## 0.016 -0.532 -0.094
##
## 0.203 0.073 0.032
##
## 0.015 0.411 0.067
##
## 0.016 0.755 0.069
##
## 0.011 0.987 0.081
##
## 0.012 0.789 0.080
##
## 0.001 -0.458 -0.144
## 0.022 0.211 0.086
##
## 0.012 0.728 0.072
##
## 0.013 0.720 0.071
##
## 0.011 0.711 0.076
##
## 0.000 -0.796 -0.213
##
## 0.007 -0.786 -0.102
##
## 0.008 -0.439 -0.103
##
## 0.014 -0.979 -0.092
##
## 0.005 0.715 0.091
##
## 0.000 0.233 0.112
##
## 0.021 -0.208 -0.061
##
## 0.039 -0.294 -0.052
##
## 0.056 0.538 0.060
##
## 0.015 -0.426 -0.066
##
## 0.011 0.651 0.069
##
## 0.019 -0.758 -0.080
##
## 0.009 -0.657 -0.081
##
## 0.028 -0.749 -0.109
##
## 0.018 -0.556 -0.102
##
## 0.003 0.154 0.055
##
## 0.084 0.514 0.542
##
## 0.010 0.110 0.068
##
## 0.015 0.668 0.075
##
## 0.016 0.425 0.065
##
## 0.013 0.594 0.074
##
## 0.010 -0.674 -0.078
##
## 0.006 0.857 0.086
##
## 0.014 0.817 0.075
##
## 0.016 -0.715 -0.084
##
## 0.000 -1.973 -4.937
## 0.068 0.133 0.130
##
## 0.315 -0.191 -0.035
##
## 0.008 0.059 0.056
##
## 0.017 0.570 0.066
##
## 0.014 0.505 0.101
##
## 0.000 -1.732 -0.541
##
## 0.000 1.542 0.418
##
## 0.000 0.558 0.214
##
## 0.000 2.364 0.527
##
## 0.000 1.339 0.296
##
## 0.000 -1.964 -0.525
## 0.002 -1.195 -0.170
## 0.000 0.964 0.226
## 0.006 -0.767 -0.142
##
## 0.000 0.521 0.276
##
## 0.000 -0.554 -0.603
##
## 0.001 -0.229 -0.092
##
## 0.002 -0.772 -0.216
##
## 0.004 -1.179 -0.100
##
## 0.008 0.691 0.077
##
## 0.006 0.505 0.102
##
## 0.012 0.635 0.065
##
## 0.008 1.036 0.081
##
## 0.011 0.779 0.075
##
## 0.019 -0.785 -0.083
##
## 0.015 0.456 0.064
##
## 0.018 -0.833 -0.082
##
## 0.012 0.755 0.077
##
## 0.019 1.037 0.079
##
## 0.020 0.677 0.070
##
## 0.043 0.181 0.050
##
## 0.014 1.050 0.077
##
## 0.020 0.823 0.069
##
## 0.012 -0.891 -0.081
##
## 0.007 0.249 0.064
##
## 0.015 0.890 0.076
##
## 0.014 -0.820 -0.079
##
## 0.025 0.538 0.065
##
## 0.023 -0.668 -0.077
##
## 0.023 -0.890 -0.072
##
## 0.026 0.407 0.057
##
## 0.024 -0.600 -0.080
##
## 0.016 0.869 0.073
##
## 0.179 -0.103 -0.035
##
## 0.017 1.019 0.070
##
## 0.090 -0.468 -0.062
##
## 0.030 -0.649 -0.068
##
## 0.024 -0.859 -0.066
##
## 0.010 -1.194 -0.085
##
## 0.024 0.687 0.067
##
## 0.020 0.748 0.073
##
## 0.458 -0.307 -0.347
##
## 0.000 -0.184 -0.201
##
## 0.033 0.094 0.065
##
## 0.001 0.187 0.117
##
## 0.016 0.058 0.031
##
## 0.149 0.300 0.048
##
## 0.137 0.096 0.028
##
## 0.024 -0.883 -0.072
##
## 0.022 0.636 0.069
##
## 0.026 -0.488 -0.085
##
## 0.024 -1.049 -0.091
##
## 0.032 -0.129 -0.039
##
## 0.021 0.404 0.069
##
## 0.028 -0.916 -0.069
##
## 0.029 0.739 0.062
##
## 0.029 0.254 0.056
##
## 0.014 0.747 0.076
##
## 0.003 0.434 0.093
##
## 0.000 1.378 0.564
## 0.000 0.575 0.236
## 0.000 0.610 0.371
## 0.000 0.679 0.254
##
## 0.000 0.450 0.256
##
## 0.000 -0.575 -0.234
##
## 0.057 0.110 0.042
##
## 0.018 0.244 0.057
##
## 0.014 0.770 0.078
##
## 0.000 -0.224 -0.223
##
## 0.143 -0.453 -0.078
## 0.038 -0.470 -0.121
## 0.005 -0.771 -0.164
##
## 0.015 -0.118 -0.093
##
## 0.066 -0.328 -0.070
##
## 0.005 0.768 0.078
##
## 0.012 -0.802 -0.085
##
## 0.314 -0.084 -0.025
##
## 0.022 0.251 0.059
##
## 0.018 0.815 0.070
##
## 0.035 0.442 0.058
##
## 0.019 -0.900 -0.087
##
## 0.005 1.328 0.095
##
## 0.028 0.762 0.065
##
## 0.128 -0.383 -0.058
##
## 0.031 0.393 0.058
##
## 0.051 -0.668 -0.061
##
## 0.033 -0.664 -0.073
##
## 0.029 -0.622 -0.073
##
## 0.046 -0.619 -0.126
##
## 0.042 -0.430 -0.058
##
## 0.038 0.372 0.057
##
## 0.018 -0.692 -0.081
##
## 0.003 0.983 0.166
## 0.065 0.376 0.067
## 0.013 -0.186 -0.057
## 0.009 0.561 0.086
##
## 0.102 0.194 0.068
##
## 0.002 0.557 0.138
##
## 0.065 0.377 0.064
##
## 0.000 0.471 0.230
##
## 0.121 -0.372 -0.058
##
## 0.570 0.055 0.021
##
## 0.138 -0.258 -0.050
##
## 0.115 0.476 0.088
##
## 0.013 0.717 0.075
##
## 0.191 0.146 0.035
##
## 0.000 0.195 0.297
## 0.000 -0.499 -0.189
##
## 0.015 -0.129 -0.068
##
## 0.011 0.697 0.075
##
## 0.066 0.862 0.056
##
## 0.190 -0.467 -0.066
##
## 0.034 0.581 0.064
##
## 0.053 0.367 0.055
##
## 0.036 -0.405 -0.065
## 0.040 -0.455 -0.091
##
## 0.042 -1.020 -0.068
##
## 0.038 -0.764 -0.064
##
## 0.069 -0.239 -0.042
##
## 0.039 -0.960 -0.071
##
## 0.036 0.745 0.058
##
## 0.043 -0.692 -0.065
##
## 0.036 -0.153 -0.059
##
## 0.161 -0.097 -0.033
##
## 0.021 -0.929 -0.069
##
## 0.043 0.335 0.050
##
## 0.011 -0.184 -0.062
##
## 0.042 -0.444 -0.062
##
## 0.024 0.277 0.057
##
## 0.078 0.116 0.042
##
## 0.041 -0.694 -0.070
##
## 0.039 -0.877 -0.064
##
## 0.599 -0.069 -0.018
##
## 0.103 0.465 0.071
##
## 0.030 0.173 0.048
##
## 0.046 0.389 0.065
##
## 0.112 0.289 0.050
##
## 0.042 -0.726 -0.064
##
## 0.029 -0.728 -0.070
##
## 0.044 0.435 0.059
##
## 0.049 0.522 0.057
##
## 0.041 -0.652 -0.064
## 0.039 0.672 0.060
##
## 0.105 0.175 0.036
##
## 0.301 -0.240 -0.035
##
## 0.036 0.216 0.053
##
## 0.057 -0.355 -0.055
##
## 0.035 0.293 0.072
## 0.000 -0.264 -0.225
##
## 0.050 -0.720 -0.064
##
## 0.046 0.758 0.060
##
## 0.044 0.427 0.052
##
## 0.041 0.679 0.060
##
## 0.044 0.378 0.051
##
## 0.196 -0.253 -0.048
##
## 0.046 -0.937 -0.061
##
## 0.048 -0.786 -0.058
##
## 0.047 -0.679 -0.065
##
## 0.060 0.332 0.051
##
## 0.039 0.663 0.062
##
## 0.023 -0.712 -0.083
##
## 0.000 1.070 0.373
## 0.000 0.403 0.230
## 0.000 0.864 0.348
##
## 0.000 0.841 0.275
##
## 0.000 1.601 0.388
##
## 0.000 0.260 0.158
## 0.000 0.495 0.393
##
## 0.000 4.127 0.489
##
## 0.000 3.459 0.338
##
## 0.000 0.610 0.248
##
## 0.000 1.327 0.194
##
## 0.000 2.077 0.279
##
## 0.000 0.271 0.126
##
## 0.000 1.700 0.182
##
## 0.000 1.636 0.204
##
## 0.000 0.898 0.179
##
## 0.000 2.958 0.207
##
## 0.000 1.565 0.173
##
## 0.000 0.434 0.145
##
## 0.000 0.660 0.181
##
## 0.000 1.652 1.880
##
## 0.570 -0.021 -0.015
##
## 0.000 1.890 1.831
##
## 0.000 0.141 0.280
##
## 0.000 -0.123 -0.137
##
## 0.001 0.071 0.049
##
## 0.000 -0.198 -0.085
##
## 0.000 1.052 0.138
##
## 0.000 0.088 0.074
##
## 0.000 1.949 1.606
##
## 0.001 -0.388 -0.148
##
## 0.000 0.309 0.095
##
## 0.011 -0.268 -0.080
##
## 0.000 0.157 0.079
##
## 0.003 0.489 0.122
##
## 0.000 0.361 0.141
##
## 0.000 0.815 0.122
##
## 0.000 1.443 0.140
##
## 0.000 1.285 0.128
##
## 0.000 0.716 0.109
##
## 0.000 1.464 0.160
##
## 0.000 0.875 0.127
##
## 0.000 -1.373 -0.116
##
## 0.003 -0.121 -0.085
##
## 0.000 -1.393 -0.129
##
## 0.000 0.480 0.089
##
## 0.000 1.593 0.166
##
## 0.543 -0.022 -0.010
##
## 0.001 -0.089 -0.086
##
## 0.013 -0.434 -0.137
##
## 0.005 -0.287 -0.097
##
## 0.000 0.994 0.104
##
## 0.001 0.656 0.092
##
## 0.001 1.031 0.107
##
## 0.001 -0.452 -0.124
##
## 0.001 0.552 0.092
##
## 0.001 0.738 0.099
##
## 0.001 0.455 0.087
##
## 0.001 0.130 0.049
##
## 0.001 -1.359 -0.111
##
## 0.001 0.642 0.094
##
## 0.001 1.155 0.106
##
## 0.001 0.408 0.085
##
## 0.001 -1.413 -0.108
##
## 0.001 -1.951 -0.126
##
## 0.001 0.842 0.105
##
## 0.001 -1.722 -0.115
##
## 0.001 0.892 0.096
##
## 0.001 1.330 0.107
##
## 0.001 0.412 0.084
##
## 0.001 0.838 0.099
##
## 0.001 1.204 0.103
##
## 0.001 1.917 0.120
##
## 0.001 0.771 0.123
##
## 0.000 -1.527 -1.355
##
## 0.001 0.697 0.094
##
## 0.001 1.207 0.105
##
## 0.001 1.503 0.111
##
## 0.001 0.155 0.098
##
## 0.002 -1.294 -0.100
##
## 0.002 -1.334 -0.106
##
## 0.002 -1.061 -0.108
##
## 0.002 1.059 0.099
##
## 0.002 0.165 0.060
##
## 0.014 0.296 0.083
##
## 0.001 -0.894 -0.113
##
## 0.002 -0.240 -0.086
##
## 0.002 1.146 0.087
##
## 0.000 -0.949 -0.136
##
## 0.003 0.398 0.084
##
## 0.003 0.210 0.055
##
## 0.006 0.127 0.065
##
## 0.035 -0.192 -0.055
##
## 0.075 -0.211 -0.053
##
## 0.130 -0.231 -0.041
##
## 0.063 -0.086 -0.042
##
## 0.041 -0.369 -0.078
## 0.048 -0.387 -0.098
##
## 0.031 0.680 0.099
##
## 0.031 -0.827 -0.115
##
## 0.049 0.282 0.078
## 0.045 0.238 0.065
##
## 0.021 0.590 0.073
##
## 0.028 0.113 0.059
##
## 0.000 0.894 0.135
## 0.028 0.829 0.071
##
## 0.023 0.117 0.048
##
## 0.033 0.761 0.065
##
## 0.034 -0.509 -0.068
##
## 0.047 -0.622 -0.069
##
## 0.023 0.975 0.858
##
## 0.041 -0.636 -0.073
##
## 0.045 -0.508 -0.060
##
## 0.050 -0.514 -0.079
##
## 0.041 0.505 0.060
##
## 0.048 -0.532 -0.065
##
## 0.054 0.340 0.054
##
## 0.041 0.111 0.038
##
## 0.010 0.657 0.076
##
## 0.570 0.129 0.022
##
## 0.063 0.318 0.052
##
## 0.286 -0.088 -0.090
## 0.142 -0.138 -0.542
## 0.036 -0.126 -0.127
##
## 0.211 0.126 0.494
## 0.001 0.246 0.249
##
## 0.228 0.097 0.378
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Bfdbctrm_l...3 -1.083 1.296 -0.835 0.404 -1.083 -0.277
## .Bct_.SGB1877.3 0.111 0.687 0.162 0.872 0.111 0.053
## .Bct_.SGB1855.3 -0.867 1.277 -0.679 0.497 -0.867 -0.245
## .Bct_.SGB1861.3 0.219 0.830 0.264 0.792 0.219 0.091
## .Bf_.SGB17278.3 -1.083 0.862 -1.256 0.209 -1.083 -0.426
## .Bf_.SGB17234.3 0.889 1.036 0.858 0.391 0.889 0.274
## .Cls_.SGB4037.3 -0.497 0.931 -0.534 0.593 -0.497 -0.182
## .C__C5_48.SGB47 -1.130 0.488 -2.314 0.021 -1.130 -0.753
## .Eg_.SGB14809.3 -0.385 1.130 -0.340 0.733 -0.385 -0.112
## .Ent_.SGB4760.3 -0.857 0.541 -1.584 0.113 -0.857 -0.560
## .Ent_.SGB7931.3 -1.225 0.735 -1.666 0.096 -1.225 -0.613
## .Ent_.SGB7962.3 3.663 1.129 3.246 0.001 3.663 0.985
## .Ebc_.SGB4933.3 0.355 0.903 0.393 0.695 0.355 0.366
## .Fc_.SGB15316.3 -0.395 0.444 -0.889 0.374 -0.395 -0.374
## .Hng_.SGB4742.3 -0.740 0.882 -0.839 0.402 -0.740 -0.347
## .Mdt_.SGB4563.3 0.063 0.669 0.095 0.925 0.063 0.044
## .Mgs_.SGB5868.3 -0.290 0.472 -0.613 0.540 -0.290 -0.254
## .Phc_.SGB1815.3 0.655 0.933 0.702 0.483 0.655 0.238
## .Phc_.SGB1814.3 0.425 0.984 0.432 0.666 0.425 0.144
## .Rt_.SGB16985.3 1.674 0.746 2.243 0.025 1.674 0.701
## .Rmn_.SGB4584.3 -0.340 1.400 -0.243 0.808 -0.340 -0.083
## .Rmn_.SGB4608.3 -0.266 0.500 -0.532 0.595 -0.266 -0.201
## .Rt_.SGB15271.3 -0.320 0.702 -0.456 0.649 -0.320 -0.164
## .Str_.SGB8065.3 2.489 1.104 2.254 0.024 2.489 0.704
## .Str_.SGB8168.3 2.164 0.650 3.326 0.001 2.164 1.023
## .Str_.SGB8084.3 1.068 0.647 1.652 0.098 1.068 0.497
## .Str_.SGB8005.3 3.300 1.075 3.069 0.002 3.300 0.978
## .St_.SGB8007_.3 3.552 1.047 3.391 0.001 3.552 1.099
## .Str_.SGB8002.3 -0.296 0.750 -0.395 0.693 -0.296 -0.125
## .Vll_.SGB6936.3 0.549 0.683 0.804 0.421 0.549 0.259
## .Bfdbctrm_l...1 5.493 1.368 4.017 0.000 5.493 1.438
## .Bct_.SGB1877.1 0.667 0.867 0.769 0.442 0.667 0.246
## .Bct_.SGB1855.1 -1.415 1.382 -1.024 0.306 -1.415 -0.377
## .Bct_.SGB1861.1 -0.559 0.953 -0.587 0.557 -0.559 -0.195
## .Bf_.SGB17278.1 4.940 1.238 3.991 0.000 4.940 1.231
## .Bf_.SGB17234.1 6.172 1.190 5.189 0.000 6.172 1.966
## .Cls_.SGB4037.1 4.835 1.124 4.302 0.000 4.835 1.374
## .C__C5_48.SGB47 0.654 1.198 0.546 0.585 0.654 0.198
## .Eg_.SGB14809.1 3.245 1.019 3.186 0.001 3.245 1.048
## .Ent_.SGB4760.1 0.031 0.760 0.041 0.967 0.031 0.014
## .Ent_.SGB7931.1 2.619 0.936 2.798 0.005 2.619 1.017
## .Ent_.SGB7962.1 0.842 0.685 1.228 0.219 0.842 0.414
## .Ebc_.SGB4933.1 -4.818 1.427 -3.376 0.001 -4.818 -1.293
## .Fc_.SGB15316.1 -4.406 1.942 -2.269 0.023 -4.406 -1.036
## .Hng_.SGB4742.1 0.652 1.000 0.652 0.515 0.652 0.224
## .Mdt_.SGB4563.1 -0.228 1.167 -0.196 0.845 -0.228 -0.068
## .Mgs_.SGB5868.1 3.827 0.900 4.250 0.000 3.827 1.485
## .Phc_.SGB1815.1 -0.090 1.122 -0.080 0.936 -0.090 -0.028
## .Phc_.SGB1814.1 1.424 1.239 1.149 0.250 1.424 0.369
## .Rt_.SGB16985.1 0.218 0.438 0.498 0.619 0.218 0.265
## .Rmn_.SGB4584.1 7.344 1.062 6.917 0.000 7.344 2.360
## .Rmn_.SGB4608.1 -4.064 1.106 -3.674 0.000 -4.064 -1.377
## .Rt_.SGB15271.1 -2.251 1.159 -1.942 0.052 -2.251 -0.761
## .Str_.SGB8065.1 1.609 0.716 2.248 0.025 1.609 0.839
## .Str_.SGB8168.1 0.228 0.385 0.593 0.553 0.228 0.389
## .Str_.SGB8084.1 0.158 0.393 0.402 0.687 0.158 0.196
## .Str_.SGB8005.1 2.629 1.010 2.602 0.009 2.629 0.828
## .St_.SGB8007_.1 -4.072 1.245 -3.270 0.001 -4.072 -1.193
## .Str_.SGB8002.1 3.961 1.247 3.175 0.001 3.961 0.974
## .Vll_.SGB6936.1 4.191 0.965 4.342 0.000 4.191 1.428
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## overweight|t1 -0.070 0.518 -0.136 0.892 -0.070 -0.069
## asthma5y_ep|t1 1.551 0.617 2.514 0.012 1.551 1.524
## Tscor_bnrml|t1 2.782 0.627 4.439 0.000 2.782 2.671
## atopy5y|t1 0.578 0.489 1.182 0.237 0.578 0.551
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .Bfdbctrm_l...3 15.285 1.293 11.819 0.000 15.285 0.997
## .Bct_.SGB1877.3 4.404 0.329 13.401 0.000 4.404 0.999
## .Bct_.SGB1855.3 12.490 1.455 8.582 0.000 12.490 1.000
## .Bct_.SGB1861.3 5.792 0.497 11.646 0.000 5.792 1.000
## .Bf_.SGB17278.3 6.004 0.322 18.635 0.000 6.004 0.929
## .Bf_.SGB17234.3 10.314 0.771 13.375 0.000 10.314 0.979
## .Cls_.SGB4037.3 5.906 0.416 14.193 0.000 5.906 0.789
## .C__C5_48.SGB47 1.746 0.083 21.096 0.000 1.746 0.777
## .Eg_.SGB14809.3 11.000 1.237 8.895 0.000 11.000 0.925
## .Ent_.SGB4760.3 1.551 0.137 11.343 0.000 1.551 0.663
## .Ent_.SGB7931.3 3.003 0.230 13.046 0.000 3.003 0.753
## .Ent_.SGB7962.3 13.827 1.276 10.837 0.000 13.827 1.000
## .Ebc_.SGB4933.3 0.791 0.044 17.814 0.000 0.791 0.843
## .Fc_.SGB15316.3 1.047 0.036 29.132 0.000 1.047 0.937
## .Hng_.SGB4742.3 3.979 0.233 17.093 0.000 3.979 0.877
## .Mdt_.SGB4563.3 1.987 0.097 20.433 0.000 1.987 0.969
## .Mgs_.SGB5868.3 1.023 0.046 22.357 0.000 1.023 0.787
## .Phc_.SGB1815.3 3.413 0.550 6.200 0.000 3.413 0.450
## .Phc_.SGB1814.3 8.742 0.821 10.649 0.000 8.742 1.000
## .Rt_.SGB16985.3 5.695 0.522 10.917 0.000 5.695 0.999
## .Rmn_.SGB4584.3 15.668 1.599 9.801 0.000 15.668 0.925
## .Rmn_.SGB4608.3 1.745 0.083 21.060 0.000 1.745 0.993
## .Rt_.SGB15271.3 3.725 0.299 12.456 0.000 3.725 0.980
## .Str_.SGB8065.3 12.515 1.399 8.945 0.000 12.515 1.000
## .Str_.SGB8168.3 4.402 0.351 12.554 0.000 4.402 0.983
## .Str_.SGB8084.3 4.616 0.382 12.093 0.000 4.616 0.999
## .Str_.SGB8005.3 11.391 1.213 9.394 0.000 11.391 1.000
## .St_.SGB8007_.3 10.431 0.963 10.837 0.000 10.431 1.000
## .Str_.SGB8002.3 5.314 0.344 15.432 0.000 5.314 0.952
## .Vll_.SGB6936.3 4.479 0.273 16.400 0.000 4.479 1.000
## .Bfdbctrm_l...1 14.453 1.212 11.924 0.000 14.453 0.991
## .Bct_.SGB1877.1 7.338 0.778 9.433 0.000 7.338 1.000
## .Bct_.SGB1855.1 14.097 1.724 8.177 0.000 14.097 1.000
## .Bct_.SGB1861.1 8.208 0.872 9.409 0.000 8.208 0.997
## .Bf_.SGB17278.1 16.106 1.801 8.945 0.000 16.106 1.000
## .Bf_.SGB17234.1 9.832 0.860 11.436 0.000 9.832 0.998
## .Cls_.SGB4037.1 12.384 1.147 10.799 0.000 12.384 1.000
## .C__C5_48.SGB47 10.912 1.257 8.681 0.000 10.912 1.000
## .Eg_.SGB14809.1 9.579 0.746 12.840 0.000 9.579 1.000
## .Ent_.SGB4760.1 4.105 0.358 11.455 0.000 4.105 0.822
## .Ent_.SGB7931.1 6.618 0.598 11.072 0.000 6.618 0.998
## .Ent_.SGB7962.1 3.391 0.275 12.354 0.000 3.391 0.820
## .Ebc_.SGB4933.1 13.886 1.682 8.254 0.000 13.886 0.999
## .Fc_.SGB15316.1 18.072 2.869 6.298 0.000 18.072 0.999
## .Hng_.SGB4742.1 8.439 0.835 10.112 0.000 8.439 0.994
## .Mdt_.SGB4563.1 11.400 1.198 9.514 0.000 11.400 1.000
## .Mgs_.SGB5868.1 6.511 0.625 10.412 0.000 6.511 0.980
## .Phc_.SGB1815.1 10.184 1.170 8.704 0.000 10.184 1.000
## .Phc_.SGB1814.1 14.872 1.871 7.949 0.000 14.872 0.999
## .Rt_.SGB16985.1 0.677 0.041 16.696 0.000 0.677 1.000
## .Rmn_.SGB4584.1 -0.091 1.067 -0.086 0.932 -0.091 -0.009
## .Rmn_.SGB4608.1 8.564 0.802 10.672 0.000 8.564 0.983
## .Rt_.SGB15271.1 8.711 0.997 8.742 0.000 8.711 0.996
## .Str_.SGB8065.1 3.676 0.265 13.882 0.000 3.676 1.000
## .Str_.SGB8168.1 0.247 0.030 8.129 0.000 0.247 0.717
## .Str_.SGB8084.1 0.604 0.030 19.892 0.000 0.604 0.931
## .Str_.SGB8005.1 10.009 1.007 9.936 0.000 10.009 0.994
## .St_.SGB8007_.1 11.649 1.117 10.426 0.000 11.649 1.000
## .Str_.SGB8002.1 16.507 1.733 9.526 0.000 16.507 0.999
## .Vll_.SGB6936.1 8.540 0.750 11.391 0.000 8.540 0.991
## .overweight 0.981 0.981 0.946
## .asthma5y_ep 0.975 0.975 0.941
## .Tscore_abnorml 0.066 0.066 0.061
## .atopy5y 0.999 0.999 0.906
## .microbiome 0.041 0.026 1.578 0.114 0.959 0.959
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ind_overweight -0.015 0.004 -3.354 0.001 -0.015 -0.010
## total_overwght -0.078 0.070 -1.113 0.266 -0.078 -0.054
## ind_asthma5y_p -0.017 0.005 -3.127 0.002 -0.017 -0.012
## total_sthm5y_p -0.133 0.090 -1.489 0.137 -0.133 -0.092
## ind_Tscr_bnrml -0.103 0.027 -3.833 0.000 -0.103 -0.070
## ttl_Tscr_bnrml -0.305 0.108 -2.817 0.005 -0.305 -0.206
## ind_atopy5y 0.003 0.002 1.309 0.190 0.003 0.002
## total_atopy5y -0.058 0.067 -0.857 0.391 -0.058 -0.039##Get the features associated with metacyc pathways
SES.maaslin.combine <- unique(SES.maaslin)
######################################################################
#Combine the models with and without interaction. If slope not significant (p-value > 0.05), use the models without interaction.
qval.ct <- 0.1
exclude.val <- c("toronto","vancouver","winnipeg","square_age","exact_age","ProcessingPeriod","1")
check.var.use <- unique(SES.maaslin.combine$check.var)
SES.masslin.all <- NULL
adj.vars.basics <- list(c("ProcessingPeriod", "exact_age","site"))
for(BFsub.use in c("All","AnyBF")){
for(adj.vars.basic.use in unique(SES.maaslin.combine$adjbasic)){
for(norm.use in c("MCLR")){
SES.masslin.new <- subset(SES.maaslin.combine, BFsub==BFsub.use & Race.Adj=="none" &
Normalization==norm.use & adjbasic==adj.vars.basic.use &
Visit=="longitudinal" & Data=="META" & Prevalence_cutoff==0.1)
for(check.var.i in check.var.use){
ses.sp.int <- subset(SES.masslin.new, check.var%in%check.var.i & value%in%c("time_interaction") & !value%in%exclude.val & Model=="Interact" )
ses.sp.main <- subset(SES.masslin.new, check.var%in%check.var.i & !value%in%exclude.val & Model=="Nointeract" )
ff.no.int <- subset(ses.sp.int, pval>=0.05)
ff.sig.int <- subset(ses.sp.int, pval<0.05)
ses.sp.int.all <- subset(SES.masslin.new, check.var%in%check.var.i & value%in%c("time_interaction",check.var.i,"exact_age") &
Model=="Interact" )
ses.sp.main.all <- subset(SES.masslin.new, check.var%in%check.var.i & value%in%c(check.var.i,"exact_age") & Model=="Nointeract")
ses.new.i <- rbind(ses.sp.int.all,
ses.sp.main.all)
SES.masslin.all <- rbind(SES.masslin.all, ses.new.i)
}
}
}
}
SES.masslin.all$feature_update <- strsplit2(SES.masslin.all$feature,"__")[,1]
dup.name <- strsplit2(unique(SES.masslin.all$feature),"__")[,1][duplicated(strsplit2(unique(SES.masslin.all$feature),"__")[,1])]
SES.masslin.all$feature_update[SES.masslin.all$feature_update %in%dup.name] <- SES.masslin.all$feature[SES.masslin.all$feature_update %in%dup.name]
SES.masslin.all$feature_update <- gsub(" "," ",gsub("_"," ",SES.masslin.all$feature_update))
SES.masslin.all <- unique(SES.masslin.all)
SES.masslin.all.use <- subset(SES.masslin.all, Normalization=="MCLR" & BFsub=="All" &
adjbasic=="ProcessingPeriod + exact_age" )Generate bubble figure of pathways to compare the effect of BF and SES (Figure S3C)
######################################################################
#Longitudinal analysis results for SES & BF narrow down to focus - Bubble plot
################################################################################
bf.sig.use.main <- unique(c(subset(SES.masslin.all.use,
qval<qval.ct & Model=="Nointeract"&
value%in%c("MR1_latent.raw","bf") &
!value%in%exclude.val )$feature))
bf.sig.use.slope <- unique(subset(SES.masslin.all.use,
qval<qval.ct & Model=="Interact"&
check.var%in%c("MR1_latent.raw","bf") & value=="time_interaction"&
!value%in%exclude.val )$feature)
all.sp.bf <- rbind(subset(SES.masslin.all.use, !value%in%exclude.val & feature%in%bf.sig.use.slope & check.var%in%c("MR1_latent.raw","bf") & value=="time_interaction" & Model=="Interact"),
subset(SES.masslin.all.use, !value%in%exclude.val & feature%in%bf.sig.use.main & check.var%in%c("MR1_latent.raw","bf") & value%in%c("MR1_latent.raw","bf")& Model=="Nointeract"))
all.sp.bf$U <- ifelse(all.sp.bf$qval<0.1,"*","")
all.sp.bf$Effect <- ifelse(all.sp.bf$value=="time_interaction","Slope",
ifelse(all.sp.bf$Model=="Interact", "Baseline",
ifelse(all.sp.bf$Model=="Nointeract", "Overall",NA)))
all.sp.bf <- merge(all.sp.bf, data.map, by.x="check.var", by.y="Vars",all.x=T)
all.sp.bf$Effect <- factor(all.sp.bf$Effect, levels=c( "Slope","Overall"), ordered=T)
all.sp.bf$Label[all.sp.bf$Label=="SES Index"] <- "SES"
all.sp.bf$Label <- factor(all.sp.bf$Label, levels=c( "SES","Breastfeeding"), ordered=T)
all.sp.bf$Direction <- ifelse(all.sp.bf$coef>0,"Positive","Negative")
all.sp.bf$sig <- ifelse(all.sp.bf$qval<0.1,"sig","nosig")
table(all.sp.bf$check.var,all.sp.bf$metadata,useNA = "ifany")##
## bf MR1_latent.raw time_interaction
## bf 235 0 231
## MR1_latent.raw 0 235 231#Dot plot
all.sp.bf.wide <- reshape2::dcast(all.sp.bf, feature +Effect ~ Label, value.var = "coef")
#To play add the mean relative
data.sp <- as.matrix(otu_table(RBMETA_MCLR))
data.meta <- data.frame(sample_data(RBMETA_MCLR))
data.sp.3m <- as.matrix(otu_table(subset_samples(RBMETA_MCLR, Visit=="3 month")))
data.sp.1y <- as.matrix(otu_table(subset_samples(RBMETA_MCLR, Visit=="1 year")))
rb.data <- merge(merge(data.frame(total_mean=apply(data.sp,2,mean)),
data.frame(m3_mean=apply(data.sp.3m,2,mean)), by="row.names"),
data.frame(y1_mean=apply(data.sp.1y,2,mean)),by.x="Row.names", by.y="row.names")
rb.data$Row.names <- gsub(" ","\\.",rb.data$Row.names)
all.sp.bf.wide <- merge(all.sp.bf.wide, rb.data, by.x="feature", by.y="Row.names",all.x=T)
data.sp.tax <- as.matrix(tax_table(RBMETA_MCLR))
rownames(data.sp.tax) <- gsub(" ","\\.",rownames(data.sp.tax))
all.sp.bf.wide <- merge(all.sp.bf.wide,data.sp.tax ,by.x="feature", by.y="row.names")
sesbf.p <- ggplot(data=all.sp.bf.wide, aes(x=SES, y=Breastfeeding)) +
geom_hline(yintercept = 0, color="grey")+
geom_vline(xintercept = 0, color="grey")+
geom_point(aes(size=total_mean,color="black")) + #
geom_smooth(method="lm", color="black")+
scale_color_cosmic()+
theme(aspect.ratio = 1)+
facet_grid(~Effect,scale="free",space="free_y")+
theme_bw()+
stat_cor( geom = "label")
plot(sesbf.p)
pdf("Figures/P5 Maaslin2 Bubble figure of metacypathways for SES and BF.pdf", height=6, width=12)
plot(sesbf.p)
dev.off()## quartz_off_screen
## 2